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language</title> <meta name="description" content="Search results for: natural language"> <meta name="keywords" content="natural language"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="natural language"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 9089</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: natural language</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9089</span> Morphology of Cartographic Words: A Perspective from Chinese Characters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xinyu%20Gong">Xinyu Gong</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhilin%20Li"> Zhilin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Xintao%20Liu"> Xintao Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Maps are a means of communication. Cartographic language involves established theories of natural language for understanding maps. “Cartographic words’, or “map symbols”, are crucial elements of cartographic language. Personalized mapping is increasingly popular, with growing demands for customized map-making by the general public. Automated symbol-making and customization play a key role in personalized mapping. However, formal representations for the automated construction of map symbols are still lacking. In natural language, the process of word and sentence construction can be formalized. Through the analogy between natural language and graphical language, formal representations of natural language construction can be used as a reference for constructing cartographic language. We selected Chinese character structures (i.e., S <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=personalized%20mapping" title="personalized mapping">personalized mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese%20character" title=" Chinese character"> Chinese character</a>, <a href="https://publications.waset.org/abstracts/search?q=cartographic%20language" title=" cartographic language"> cartographic language</a>, <a href="https://publications.waset.org/abstracts/search?q=map%20symbols" title=" map symbols"> map symbols</a> </p> <a href="https://publications.waset.org/abstracts/131340/morphology-of-cartographic-words-a-perspective-from-chinese-characters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131340.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9088</span> Models and Metamodels for Computer-Assisted Natural Language Grammar Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evgeny%20Pyshkin">Evgeny Pyshkin</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxim%20Mozgovoy"> Maxim Mozgovoy</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladislav%20Volkov"> Vladislav Volkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper follows a discourse on computer-assisted language learning. We examine problems of foreign language teaching and learning and introduce a metamodel that can be used to define learning models of language grammar structures in order to support teacher/student interaction. Special attention is paid to the concept of a virtual language lab. Our approach to language education assumes to encourage learners to experiment with a language and to learn by discovering patterns of grammatically correct structures created and managed by a language expert. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer-assisted%20instruction" title="computer-assisted instruction">computer-assisted instruction</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20learning" title=" language learning"> language learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20grammar%20models" title=" natural language grammar models"> natural language grammar models</a>, <a href="https://publications.waset.org/abstracts/search?q=HCI" title=" HCI"> HCI</a> </p> <a href="https://publications.waset.org/abstracts/15680/models-and-metamodels-for-computer-assisted-natural-language-grammar-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">519</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9087</span> Impact of Natural Language Processing in Educational Setting: An Effective Approach towards Improved Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Processing (NLP) is an effective approach for bringing improvement in educational setting. This involves initiating the process of learning through the natural acquisition in the educational systems. It is based on following effective approaches for providing the solution for various problems and issues in education. Natural Language Processing provides solution in a variety of different fields associated with the social and cultural context of language learning. It is based on involving various tools and techniques such as grammar, syntax, and structure of text. It is effective approach for teachers, students, authors, and educators for providing assistance for writing, analysis, and assessment procedure. Natural Language Processing is widely integrated in the large number of educational contexts such as research, science, linguistics, e-learning, evaluations system, and various other educational settings such as schools, higher education system, and universities. Natural Language Processing is based on applying scientific approach in the educational settings. In the educational settings, NLP is an effective approach to ensure that students can learn easily in the same way as they acquired language in the natural settings. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20studies" title=" scientific studies"> scientific studies</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20system" title=" educational system"> educational system</a> </p> <a href="https://publications.waset.org/abstracts/21292/impact-of-natural-language-processing-in-educational-setting-an-effective-approach-towards-improved-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21292.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">503</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9086</span> Role of Natural Language Processing in Information Retrieval; Challenges and Opportunities </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to analyze the role of natural language processing (NLP). The paper will discuss the role in the context of automated data retrieval, automated question answer, and text structuring. NLP techniques are gaining wider acceptance in real life applications and industrial concerns. There are various complexities involved in processing the text of natural language that could satisfy the need of decision makers. This paper begins with the description of the qualities of NLP practices. The paper then focuses on the challenges in natural language processing. The paper also discusses major techniques of NLP. The last section describes opportunities and challenges for future research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20retrieval" title="data retrieval">data retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20structuring" title=" text structuring"> text structuring</a> </p> <a href="https://publications.waset.org/abstracts/21284/role-of-natural-language-processing-in-information-retrieval-challenges-and-opportunities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21284.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9085</span> Natural Language Processing; the Future of Clinical Record Management </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the future of medicine and the use of Natural language processing. The importance of having correct clinical information available online is remarkable; improving patient care at affordable costs could be achieved using automated applications to use the online clinical information. The major challenge towards the retrieval of such vital information is to have it appropriately coded. Majority of the online patient reports are not found to be coded and not accessible as its recorded in natural language text. The use of Natural Language processing provides a feasible solution by retrieving and organizing clinical information, available in text and transforming clinical data that is available for use. Systems used in NLP are rather complex to construct, as they entail considerable knowledge, however significant development has been made. Newly formed NLP systems have been tested and have established performance that is promising and considered as practical clinical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20information" title="clinical information">clinical information</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=automated%20applications" title=" automated applications"> automated applications</a> </p> <a href="https://publications.waset.org/abstracts/26320/natural-language-processing-the-future-of-clinical-record-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9084</span> A Review of Research on Pre-training Technology for Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moquan%20Gong">Moquan Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-training" title=" pre-training"> pre-training</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20vectors" title=" word vectors"> word vectors</a> </p> <a href="https://publications.waset.org/abstracts/183121/a-review-of-research-on-pre-training-technology-for-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183121.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">57</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9083</span> Context Detection in Spreadsheets Based on Automatically Inferred Table Schema</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Wachtel">Alexander Wachtel</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20T.%20Franzen"> Michael T. Franzen</a>, <a href="https://publications.waset.org/abstracts/search?q=Walter%20F.%20Tichy"> Walter F. Tichy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programming requires years of training. With natural language and end user development methods, programming could become available to everyone. It enables end users to program their own devices and extend the functionality of the existing system without any knowledge of programming languages. In this paper, we describe an Interactive Spreadsheet Processing Module (ISPM), a natural language interface to spreadsheets that allows users to address ranges within the spreadsheet based on inferred table schema. Using the ISPM, end users are able to search for values in the schema of the table and to address the data in spreadsheets implicitly. Furthermore, it enables them to select and sort the spreadsheet data by using natural language. ISPM uses a machine learning technique to automatically infer areas within a spreadsheet, including different kinds of headers and data ranges. Since ranges can be identified from natural language queries, the end users can query the data using natural language. During the evaluation 12 undergraduate students were asked to perform operations (sum, sort, group and select) using the system and also Excel without ISPM interface, and the time taken for task completion was compared across the two systems. Only for the selection task did users take less time in Excel (since they directly selected the cells using the mouse) than in ISPM, by using natural language for end user software engineering, to overcome the present bottleneck of professional developers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20interfaces" title=" natural language interfaces"> natural language interfaces</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20computer%20interaction" title=" human computer interaction"> human computer interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=end%20user%20development" title=" end user development"> end user development</a>, <a href="https://publications.waset.org/abstracts/search?q=dialog%20systems" title=" dialog systems"> dialog systems</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20recognition" title=" data recognition"> data recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=spreadsheet" title=" spreadsheet"> spreadsheet</a> </p> <a href="https://publications.waset.org/abstracts/54528/context-detection-in-spreadsheets-based-on-automatically-inferred-table-schema" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54528.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9082</span> Application of Natural Language Processing in Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reading capability is a major segment of language competency. On the other hand, discovering topical writings at a fitting level for outside and second language learners is a test for educators. We address this issue utilizing natural language preparing innovation to survey reading level and streamline content. In the connection of outside and second-language learning, existing measures of reading level are not appropriate to this errand. Related work has demonstrated the profit of utilizing measurable language preparing procedures; we expand these thoughts and incorporate other potential peculiarities to measure intelligibility. In the first piece of this examination, we join characteristics from measurable language models, customary reading level measures and other language preparing apparatuses to deliver a finer technique for recognizing reading level. We examine the execution of human annotators and assess results for our finders concerning human appraisals. A key commitment is that our identifiers are trainable; with preparing and test information from the same space, our finders beat more general reading level instruments (Flesch-Kincaid and Lexile). Trainability will permit execution to be tuned to address the needs of specific gatherings or understudies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=trainability" title=" trainability"> trainability</a>, <a href="https://publications.waset.org/abstracts/search?q=syntactic%20simplification%20tools" title=" syntactic simplification tools"> syntactic simplification tools</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/21289/application-of-natural-language-processing-in-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21289.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">490</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9081</span> JaCoText: A Pretrained Model for Java Code-Text Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jessica%20Lopez%20Espejel">Jessica Lopez Espejel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahaman%20Sanoussi%20Yahaya%20Alassan"> Mahaman Sanoussi Yahaya Alassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Dahhane"> Walid Dahhane</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Hassane%20Ettifouri"> El Hassane Ettifouri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pretrained transformer-based models have shown high performance in natural language generation tasks. However, a new wave of interest has surged: automatic programming language code generation. This task consists of translating natural language instructions to a source code. Despite the fact that well-known pre-trained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformer neural network. It aims to generate java source code from natural language text. JaCoText leverages the advantages of both natural language and code generation models. More specifically, we study some findings from state of the art and use them to (1) initialize our model from powerful pre-trained models, (2) explore additional pretraining on our java dataset, (3) lead experiments combining the unimodal and bimodal data in training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=java%20code%20generation" title="java code generation">java code generation</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence-to-sequence%20models" title=" sequence-to-sequence models"> sequence-to-sequence models</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer%20neural%20networks" title=" transformer neural networks"> transformer neural networks</a> </p> <a href="https://publications.waset.org/abstracts/156766/jacotext-a-pretrained-model-for-java-code-text-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156766.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9080</span> Gender Bias in Natural Language Processing: Machines Reflect Misogyny in Society</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irene%20Yi">Irene Yi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gendered%20grammar" title="gendered grammar">gendered grammar</a>, <a href="https://publications.waset.org/abstracts/search?q=misogynistic%20language" title=" misogynistic language"> misogynistic language</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/123692/gender-bias-in-natural-language-processing-machines-reflect-misogyny-in-society" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9079</span> Resume Ranking Using Custom Word2vec and Rule-Based Natural Language Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Sapkota"> Rajendra Sapkota</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakash%20Tamang"> Aakash Tamang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shushant%20Pudasaini"> Shushant Pudasaini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujan%20Adhikari"> Sujan Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajjan%20Adhikari"> Sajjan Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chunking" title="chunking">chunking</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20similarity" title=" document similarity"> document similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embedding" title=" word embedding"> word embedding</a> </p> <a href="https://publications.waset.org/abstracts/129534/resume-ranking-using-custom-word2vec-and-rule-based-natural-language-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9078</span> Resource Creation Using Natural Language Processing Techniques for Malay Translated Qur'an</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Diana%20Ahmad">Nor Diana Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Atwell"> Eric Atwell</a>, <a href="https://publications.waset.org/abstracts/search?q=Brandon%20Bennett"> Brandon Bennett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing techniques for English have been developed for several decades. But for the Malay language, text processing methods are still far behind. Moreover, there are limited resources, tools for computational linguistic analysis available for the Malay language. Therefore, this research presents the use of natural language processing (NLP) in processing Malay translated Qur’an text. As the result, a new language resource for Malay translated Qur’an was created. This resource will help other researchers to build the necessary processing tools for the Malay language. This research also develops a simple question-answer prototype to demonstrate the use of the Malay Qur’an resource for text processing. This prototype has been developed using Python. The prototype pre-processes the Malay Qur’an and an input query using a stemming algorithm and then searches for occurrences of the query word stem. The result produced shows improved matching likelihood between user query and its answer. A POS-tagging algorithm has also been produced. The stemming and tagging algorithms can be used as tools for research related to other Malay texts and can be used to support applications such as information retrieval, question answering systems, ontology-based search and other text analysis tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20resource" title="language resource">language resource</a>, <a href="https://publications.waset.org/abstracts/search?q=Malay%20translated%20Qur%27an" title=" Malay translated Qur'an"> Malay translated Qur'an</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29" title=" natural language processing (NLP)"> natural language processing (NLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20processing" title=" text processing"> text processing</a> </p> <a href="https://publications.waset.org/abstracts/92441/resource-creation-using-natural-language-processing-techniques-for-malay-translated-quran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92441.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9077</span> Multilingualism without a Dominant Language in the Preschool Age: A Case of Natural Italian-Russian-German-English Multilingualism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Legkikh%20Victoria">Legkikh Victoria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of keeping bi/multilingualism is usually a way to let the child speak two/three languages at the same level. The main problem which normally appears is a mixed language or a domination of one language. The same level of two or more languages would be ideal but practically not easily reachable. So it was made an experiment with a girl with a natural multilingualism as an attempt to avoid a dominant language in the preschool age. The girl lives in Germany and the main languages for her are Italian, Russian and German but she also hears every day English. ‘One parent – one language’ strategy was used since the beginning so Italian and Russian were spoken to her since her birth, English was spoken between the parents and when she was 1,5 it was added German as a language of a nursery. In order to avoid a dominant language, she was always put in international groups with activity in different languages. Even if it was not possible to avoid an interference of languages in this case we can talk not only about natural multilingualism but also about balanced bilingualism in preschool time. The languages have been developing in parallel with different accents in a different period. Now at the age of 6 we can see natural horizontal multilingualism Russian/Italian/German/English. At the moment, her Russian/Italian bilingualism is balanced. German vocabulary is less but the language is active and English is receptive. We can also see a reciprocal interference of all the three languages (English is receptive so the simple phrases are normally said correctly but they are not enough to judge the level of language interference and it is not noticed any ‘English’ mistakes in other languages). After analysis of the state of every language, we can see as a positive and negative result of the experiment. As a positive result we can see that in the age of 6 the girl does not refuse any language, three languages are active, she differentiate languages and even if she says a word from another language she notifies that it is not a correct word, and the most important are the fact, that she does not have a preferred language. As a prove of the last statement it is to be noticed not only her self-identification as ‘half Russian and half Italian’ but also an answer to the question about her ‘mother tongue’: ‘I do not know, probably, when I have my own children I will speak one day Russian and one day Italian and sometimes German’. As a negative result, we can notice that not only a development of all the three languages are a little bit slower than it is supposed for her age but since she does not have a dominating language she also does not have a ‘perfect’ language and the interference is reciprocal. In any case, the experiment shows that it is possible to keep at least two languages without a preference in a pre-school multilingual space. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=balanced%20bilingualism" title="balanced bilingualism">balanced bilingualism</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20interference" title=" language interference"> language interference</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20multilingualism" title=" natural multilingualism"> natural multilingualism</a>, <a href="https://publications.waset.org/abstracts/search?q=preschool%20multilingual%20education" title=" preschool multilingual education"> preschool multilingual education</a> </p> <a href="https://publications.waset.org/abstracts/56962/multilingualism-without-a-dominant-language-in-the-preschool-age-a-case-of-natural-italian-russian-german-english-multilingualism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56962.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">273</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9076</span> A Survey of the Applications of Sentiment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural language often conveys emotions of speakers. Therefore, sentiment analysis on what people say is prevalent in the field of natural language process and has great application value in many practical problems. Thus, to help people understand its application value, in this paper, we survey various applications of sentiment analysis, including the ones in online business and offline business as well as other types of its applications. In particular, we give some application examples in intelligent customer service systems in China. Besides, we compare the applications of sentiment analysis on Twitter, Weibo, Taobao and Facebook, and discuss some challenges. Finally, we point out the challenges faced in the applications of sentiment analysis and the work that is worth being studied in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=application" title="application">application</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20comments" title=" online comments"> online comments</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis "> sentiment analysis </a> </p> <a href="https://publications.waset.org/abstracts/128022/a-survey-of-the-applications-of-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9075</span> Literacy in First and Second Language: Implication for Language Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Inuwa%20Danladi%20Bawa">Inuwa Danladi Bawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the challenges of African states in the development of education in the past and the present is the problem of literacy. Literacy in the first language is seen as a strong base for the development of second language; they are mostly the language of education. Language development is an offshoot of language planning; so the need to develop literacy in both first and second language affects language education and predicts the extent of achievement of the entire education sector. The need to balance literacy acquisition in first language for good conditioning the acquisition of second language is paramount. Likely constraints that includes; non-standardization, underdeveloped and undeveloped first languages are among many. Solutions to some of these include the development of materials and use of the stages and levels of literacy acquisition. This is with believed that a child writes well in second language if he has literacy in the first language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=first%20language" title="first language">first language</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20language" title=" second language"> second language</a>, <a href="https://publications.waset.org/abstracts/search?q=literacy" title=" literacy"> literacy</a>, <a href="https://publications.waset.org/abstracts/search?q=english%20language" title=" english language"> english language</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistics" title=" linguistics"> linguistics</a> </p> <a href="https://publications.waset.org/abstracts/3745/literacy-in-first-and-second-language-implication-for-language-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">452</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9074</span> Revitalization of Sign Language through Deaf Theatre: A Linguistic Analysis of an Art Form Which Combines Physical Theatre, Poetry, and Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gal%20Belsitzman">Gal Belsitzman</a>, <a href="https://publications.waset.org/abstracts/search?q=Rose%20Stamp"> Rose Stamp</a>, <a href="https://publications.waset.org/abstracts/search?q=Atay%20Citron"> Atay Citron</a>, <a href="https://publications.waset.org/abstracts/search?q=Wendy%20Sandler"> Wendy Sandler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sign languages are considered endangered. The vitality of sign languages is compromised by its unique sociolinguistic situation, in which hearing parents that give birth to deaf children usually decide to cochlear implant their child. Therefore, these children don’t acquire their natural language – Sign Language. Despite this, many sign languages, such as Israeli Sign Language (ISL) are thriving. The continued survival of similar languages under threat has been associated with the remarkable resilience of the language community. In particular, deaf literary traditions are central in reminding the community of the importance of the language. One example of a deaf literary tradition which has received increased popularity in recent years is deaf theatre. The Ebisu Sign Language Theatre Laboratory, developed as part of the multidisciplinary Grammar of the Body Research Project, is the first deaf theatre company in Israel. Ebisu Theatre combines physical theatre and sign language research, to allow for a natural laboratory to analyze the creative use of the body. In this presentation, we focus on the recent theatre production called ‘Their language’ which tells of the struggle faced by the deaf community to use their own natural language in the education system. A thorough analysis unravels how linguistic properties are integrated with the use of poetic devices and physical theatre techniques in this performance, enabling wider access by both deaf and hearing audiences, without interpretation. Interviews with the audience illustrate the significance of this art form which serves a dual purpose, both as empowering for the deaf community and educational for the hearing and deaf audiences, by raising awareness of community-related issues. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deaf%20theatre" title="deaf theatre">deaf theatre</a>, <a href="https://publications.waset.org/abstracts/search?q=empowerment" title=" empowerment"> empowerment</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20revitalization" title=" language revitalization"> language revitalization</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language" title=" sign language"> sign language</a> </p> <a href="https://publications.waset.org/abstracts/99226/revitalization-of-sign-language-through-deaf-theatre-a-linguistic-analysis-of-an-art-form-which-combines-physical-theatre-poetry-and-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99226.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9073</span> Unraveling the Phonosignological Foundations of Human Language and Semantic Analysis of Linguistic Elements in Cross-Cultural Contexts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmudjon%20Kuchkarov">Mahmudjon Kuchkarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Marufjon%20Kuchkarov"> Marufjon Kuchkarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukhayyo%20Sobirjanova"> Mukhayyo Sobirjanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The origins of human language remain a profound scientific mystery, characterized by speculative theories often lacking empirical support. This study presents findings that may illuminate the genesis of human language, emphasizing its roots in natural, systematic, and repetitive sound patterns. Also, this paper presents the phonosignological and semantic analysis of linguistic elements across various languages and cultures. By utilizing the principles of the "Human Language" theory, we analyze the symbolic, phonetic, and semantic characteristics of elements such as "A", "L", "I", "F", and "四" (pronounced /si/ in Chinese and /shi/ in Japanese). Our findings reveal that natural sounds and their symbolic representations form the foundation of language, with significant implications for understanding religious and secular myths. This paper explores the intricate relationships between these elements and their cultural connotations, particularly focusing on the concept of "descent" in the context of the phonetic sequence "A, L, I, F," and the symbolic associations of the number four with death. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20research" title="empirical research">empirical research</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20language" title=" human language"> human language</a>, <a href="https://publications.waset.org/abstracts/search?q=phonosignology" title=" phonosignology"> phonosignology</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20patterns" title=" sound patterns"> sound patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=symbolism" title=" symbolism"> symbolism</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20shape" title=" body shape"> body shape</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20language" title=" body language"> body language</a>, <a href="https://publications.waset.org/abstracts/search?q=coding" title=" coding"> coding</a>, <a href="https://publications.waset.org/abstracts/search?q=Latin%20alphabet" title=" Latin alphabet"> Latin alphabet</a>, <a href="https://publications.waset.org/abstracts/search?q=merging%20method" title=" merging method"> merging method</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20sound" title=" natural sound"> natural sound</a>, <a href="https://publications.waset.org/abstracts/search?q=origin%20of%20language" title=" origin of language"> origin of language</a>, <a href="https://publications.waset.org/abstracts/search?q=pairing" title=" pairing"> pairing</a>, <a href="https://publications.waset.org/abstracts/search?q=phonetics" title=" phonetics"> phonetics</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20and%20shape%20production" title=" sound and shape production"> sound and shape production</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20origin" title=" word origin"> word origin</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20semantic" title=" word semantic"> word semantic</a> </p> <a href="https://publications.waset.org/abstracts/188357/unraveling-the-phonosignological-foundations-of-human-language-and-semantic-analysis-of-linguistic-elements-in-cross-cultural-contexts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188357.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9072</span> On Dialogue Systems Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan">Yifan Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin"> Pingping Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dialogue%20management" title="dialogue management">dialogue management</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20generation" title=" response generation"> response generation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=evaluation" title=" evaluation"> evaluation</a> </p> <a href="https://publications.waset.org/abstracts/129369/on-dialogue-systems-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129369.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9071</span> Coupling Large Language Models with Disaster Knowledge Graphs for Intelligent Construction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhengrong%20Wu">Zhengrong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Haibo%20Yang"> Haibo Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=large%20language%20model" title="large language model">large language model</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster" title=" disaster"> disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/182751/coupling-large-language-models-with-disaster-knowledge-graphs-for-intelligent-construction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182751.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">56</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9070</span> Detecting Paraphrases in Arabic Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Alshahrani">Amal Alshahrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Allan%20Ramsay"> Allan Ramsay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=TF-IDF" title=" TF-IDF"> TF-IDF</a>, <a href="https://publications.waset.org/abstracts/search?q=cosine%20similarity" title=" cosine similarity"> cosine similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20time%20warping%20%28DTW%29" title=" dynamic time warping (DTW)"> dynamic time warping (DTW)</a> </p> <a href="https://publications.waset.org/abstracts/35776/detecting-paraphrases-in-arabic-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9069</span> From User's Requirements to UML Class Diagram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zeineb%20Ben%20Azzouz">Zeineb Ben Azzouz</a>, <a href="https://publications.waset.org/abstracts/search?q=Wahiba%20Ben%20Abdessalem%20Karaa"> Wahiba Ben Abdessalem Karaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automated extraction of UML class diagram from natural language requirements is a highly challenging task. Many approaches, frameworks and tools have been presented in this field. Nonetheless, the experiments of these tools have shown that there is no approach that can work best all the time. In this context, we propose a new accurate approach to facilitate the automatic mapping from textual requirements to UML class diagram. Our new approach integrates the best properties of statistical Natural Language Processing (NLP) techniques to reduce ambiguity when analysing natural language requirements text. In addition, our approach follows the best practices defined by conceptual modelling experts to determine some patterns indispensable for the extraction of basic elements and concepts of the class diagram. Once the relevant information of class diagram is captured, a XMI document is generated and imported with a CASE tool to build the corresponding UML class diagram. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=class%20diagram" title="class diagram">class diagram</a>, <a href="https://publications.waset.org/abstracts/search?q=user%E2%80%99s%20requirements" title=" user’s requirements"> user’s requirements</a>, <a href="https://publications.waset.org/abstracts/search?q=XMI" title=" XMI"> XMI</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20engineering" title=" software engineering"> software engineering</a> </p> <a href="https://publications.waset.org/abstracts/7098/from-users-requirements-to-uml-class-diagram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7098.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">471</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9068</span> Transportation Language Register as One of Language Community</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diyah%20Atiek%20Mustikawati">Diyah Atiek Mustikawati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Language register refers to a variety of a language used for particular purpose or in a particular social setting. Language register also means as a concept of adapting one’s use of language to conform to standards or tradition in a given professional or social situation. This descriptive study tends to discuss about the form of language register in transportation aspect, factors, also the function of use it. Mostly, language register in transportation aspect uses short sentences in form of informal register. The factor caused language register used are speaker, word choice, background of language. The functions of language register in transportations aspect are to make communication between crew easily, also to keep safety when they were in bad condition. Transportation language register developed naturally as one of variety of language used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20register" title="language register">language register</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20variety" title=" language variety"> language variety</a>, <a href="https://publications.waset.org/abstracts/search?q=communication" title=" communication"> communication</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation" title=" transportation"> transportation</a> </p> <a href="https://publications.waset.org/abstracts/37039/transportation-language-register-as-one-of-language-community" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37039.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">486</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9067</span> Probing Language Models for Multiple Linguistic Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding">Bowen Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang"> Yihao Kuang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20models" title="language models">language models</a>, <a href="https://publications.waset.org/abstracts/search?q=probing%20task" title=" probing task"> probing task</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20presentation" title=" text presentation"> text presentation</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20information" title=" linguistic information"> linguistic information</a> </p> <a href="https://publications.waset.org/abstracts/168840/probing-language-models-for-multiple-linguistic-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168840.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">110</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9066</span> User Guidance for Effective Query Interpretation in Natural Language Interfaces to Ontologies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aliyu%20Isah%20Agaie">Aliyu Isah Agaie</a>, <a href="https://publications.waset.org/abstracts/search?q=Masrah%20Azrifah%20Azmi%20Murad"> Masrah Azrifah Azmi Murad</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurfadhlina%20Mohd%20Sharef"> Nurfadhlina Mohd Sharef</a>, <a href="https://publications.waset.org/abstracts/search?q=Aida%20Mustapha"> Aida Mustapha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural Language Interfaces typically support a restricted language and also have scopes and limitations that naïve users are unaware of, resulting in errors when the users attempt to retrieve information from ontologies. To overcome this challenge, an auto-suggest feature is introduced into the querying process where users are guided through the querying process using interactive query construction system. Guiding users to formulate their queries, while providing them with an unconstrained (or almost unconstrained) way to query the ontology results in better interpretation of the query and ultimately lead to an effective search. The approach described in this paper is unobtrusive and subtly guides the users, so that they have a choice of either selecting from the suggestion list or typing in full. The user is not coerced into accepting system suggestions and can express himself using fragments or full sentences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-suggest" title="auto-suggest">auto-suggest</a>, <a href="https://publications.waset.org/abstracts/search?q=expressiveness" title=" expressiveness"> expressiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=habitability" title=" habitability"> habitability</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20interface" title=" natural language interface"> natural language interface</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20interpretation" title=" query interpretation"> query interpretation</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20guidance" title=" user guidance"> user guidance</a> </p> <a href="https://publications.waset.org/abstracts/42815/user-guidance-for-effective-query-interpretation-in-natural-language-interfaces-to-ontologies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42815.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9065</span> Prompt Design for Code Generation in Data Analysis Using Large Language Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Song%20Ma%20Li%20Zhi">Lu Song Ma Li Zhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=large%20language%20models" title="large language models">large language models</a>, <a href="https://publications.waset.org/abstracts/search?q=prompt%20design" title=" prompt design"> prompt design</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=code%20generation" title=" code generation"> code generation</a> </p> <a href="https://publications.waset.org/abstracts/188761/prompt-design-for-code-generation-in-data-analysis-using-large-language-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188761.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">39</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9064</span> Generating Insights from Data Using a Hybrid Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Allmin%20Susaiyah">Allmin Susaiyah</a>, <a href="https://publications.waset.org/abstracts/search?q=Aki%20H%C3%A4rm%C3%A4"> Aki Härmä</a>, <a href="https://publications.waset.org/abstracts/search?q=Milan%20Petkovi%C4%87"> Milan Petković</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=insight%20mining" title=" insight mining"> insight mining</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20generation" title=" natural language generation"> natural language generation</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-trained%20language%20models" title=" pre-trained language models"> pre-trained language models</a> </p> <a href="https://publications.waset.org/abstracts/159536/generating-insights-from-data-using-a-hybrid-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159536.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9063</span> From the “Movement Language” to Communication Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmudjon%20Kuchkarov">Mahmudjon Kuchkarov</a>, <a href="https://publications.waset.org/abstracts/search?q=Marufjon%20Kuchkarov"> Marufjon Kuchkarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The origin of ‘Human Language’ is still a secret and the most interesting subject of historical linguistics. The core element is the nature of labeling or coding the things or processes with symbols and sounds. In this paper, we investigate human’s involuntary Paired Sounds and Shape Production (PSSP) and its contribution to the development of early human communication. Aimed at twenty-six volunteers who provided many physical movements with various difficulties, the research team investigated the natural, repeatable, and paired sounds and shape productions during human activities. The paper claims the involvement of Paired Sounds and Shape Production (PSSP) in the phonetic origin of some modern words and the existence of similarities between elements of PSSP with characters of the classic Latin alphabet. The results may be used not only as a supporting idea for existing theories but to create a closer look at some fundamental nature of the origin of the languages as well. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=body%20shape" title="body shape">body shape</a>, <a href="https://publications.waset.org/abstracts/search?q=body%20language" title=" body language"> body language</a>, <a href="https://publications.waset.org/abstracts/search?q=coding" title=" coding"> coding</a>, <a href="https://publications.waset.org/abstracts/search?q=Latin%20alphabet" title=" Latin alphabet"> Latin alphabet</a>, <a href="https://publications.waset.org/abstracts/search?q=merging%20method" title=" merging method"> merging method</a>, <a href="https://publications.waset.org/abstracts/search?q=movement%20language" title=" movement language"> movement language</a>, <a href="https://publications.waset.org/abstracts/search?q=movement%20sound" title=" movement sound"> movement sound</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20sound" title=" natural sound"> natural sound</a>, <a href="https://publications.waset.org/abstracts/search?q=origin%20of%20language" title=" origin of language"> origin of language</a>, <a href="https://publications.waset.org/abstracts/search?q=pairing" title=" pairing"> pairing</a>, <a href="https://publications.waset.org/abstracts/search?q=phonetics" title=" phonetics"> phonetics</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20and%20shape%20production" title=" sound and shape production"> sound and shape production</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20origin" title=" word origin"> word origin</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20semantic" title=" word semantic"> word semantic</a> </p> <a href="https://publications.waset.org/abstracts/160314/from-the-movement-language-to-communication-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">249</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9062</span> The Mother Tongue and Related Issues in Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farouk%20A.N.%20Bouhadiba">Farouk A.N. Bouhadiba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on Fishman’s Theoretical Paradigm (1991), we shall first discuss his three value positions for the case of the so called minority native languages in Algeria and how they may be included into a global language teaching program in Algeria. We shall then move on to his scale on language loss, language maintenance and language renewal with illustrating examples taken from the Algerian context. The second part of our talk relates to pedagogical issues on how to proceed for a smooth transition from mother tongue to school tongue, what methods or approaches suit best the teaching of mother tongue and school tongue (Immersion Programs, The Natural Approach, Applied Literacy Programs, The Berlitz Method, etc.). We shall end up our talk on how one may reshuffle the current issues on the “Arabic-only” movement and the abrupt transition from mother tongue to school tongue in use today by opting for teaching programs that involve pre-school language acquisition and in-school language acquisition grammars, and thus pave the way to effective language teaching programs and living curricula and pedagogies such as language nests, intergenerational continuity, communication and identity teaching programs, which result in better language teaching models that make language policies become a reality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=native%20languages" title="native languages">native languages</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20maintenance" title=" language maintenance"> language maintenance</a>, <a href="https://publications.waset.org/abstracts/search?q=mother%20tongue" title=" mother tongue"> mother tongue</a>, <a href="https://publications.waset.org/abstracts/search?q=school%20tongue" title=" school tongue"> school tongue</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=Algeria" title=" Algeria"> Algeria</a> </p> <a href="https://publications.waset.org/abstracts/189378/the-mother-tongue-and-related-issues-in-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189378.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">30</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9061</span> Natural Language News Generation from Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bastian%20Haarmann">Bastian Haarmann</a>, <a href="https://publications.waset.org/abstracts/search?q=Likas%20Sikorski"> Likas Sikorski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The fully automatic generated stories have a high resemblance to the style in which the human writer would draw up a news story. Topics may include soccer games, stock exchange market reports, weather forecasts and many more. The generation of the texts runs according to the human language production. Each generated text is unique. Ready-to-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save time-consuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20generation" title=" natural language generation"> natural language generation</a>, <a href="https://publications.waset.org/abstracts/search?q=publishing" title=" publishing"> publishing</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20journalism" title=" robotic journalism"> robotic journalism</a> </p> <a href="https://publications.waset.org/abstracts/27303/natural-language-news-generation-from-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27303.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">431</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9060</span> Semantic Textual Similarity on Contracts: Exploring Multiple Negative Ranking Losses for Sentence Transformers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yogendra%20Sisodia">Yogendra Sisodia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=legal%20contracts" title="legal contracts">legal contracts</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20negative%20ranking%20loss" title=" multiple negative ranking loss"> multiple negative ranking loss</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20inference" title=" natural language inference"> natural language inference</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20transformers" title=" sentence transformers"> sentence transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity" title=" semantic textual similarity"> semantic textual similarity</a> </p> <a href="https://publications.waset.org/abstracts/156624/semantic-textual-similarity-on-contracts-exploring-multiple-negative-ranking-losses-for-sentence-transformers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156624.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=natural%20language&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=natural%20language&page=3">3</a></li> <li class="page-item"><a class="page-link" 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