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information retrieval</title> <meta name="description" content="Search results for: text information retrieval"> <meta name="keywords" content="text information retrieval"> <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 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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="text information retrieval"> <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> 11819</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: text information retrieval</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11819</span> Text Data Preprocessing Library: Bilingual Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kabil%20Boukhari">Kabil Boukhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on diﬀerent corpora and gave an interesting result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20preprocessing" title="text preprocessing">text preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20generation" title=" text generation"> text generation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/150846/text-data-preprocessing-library-bilingual-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150846.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">94</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">11818</span> Urdu Text Extraction Method from Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samabia%20Tehsin">Samabia Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Kausar"> Sumaira Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=caption%20text" title="caption text">caption text</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20image%20retrieval" title=" content-based image retrieval"> content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9566/urdu-text-extraction-method-from-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9566.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">516</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">11817</span> A Framework of Product Information Service System Using Mobile Image Retrieval and Text Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mei-Yi%20Wu">Mei-Yi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shang-Ming%20Huang"> Shang-Ming Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20image%20retrieval" title="mobile image retrieval">mobile image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20information%20service%20system" title=" product information service system"> product information service system</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20marketing" title=" online marketing"> online marketing</a> </p> <a href="https://publications.waset.org/abstracts/33483/a-framework-of-product-information-service-system-using-mobile-image-retrieval-and-text-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33483.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">359</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">11816</span> Information Retrieval for Kafficho Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mareye%20Zeleke%20Mekonen">Mareye Zeleke Mekonen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Kafficho language has distinct issues in information retrieval because of its restricted resources and dearth of standardized methods. In this endeavor, with the cooperation and support of linguists and native speakers, we investigate the creation of information retrieval systems specifically designed for the Kafficho language. The Kafficho information retrieval system allows Kafficho speakers to access information easily in an efficient and effective way. Our objective is to conduct an information retrieval experiment using 220 Kafficho text files, including fifteen sample questions. Tokenization, normalization, stop word removal, stemming, and other data pre-processing chores, together with additional tasks like term weighting, were prerequisites for the vector space model to represent each page and a particular query. The three well-known measurement metrics we used for our word were Precision, Recall, and and F-measure, with values of 87%, 28%, and 35%, respectively. This demonstrates how well the Kaffiho information retrieval system performed well while utilizing the vector space paradigm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kafficho" title="Kafficho">Kafficho</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=stemming" title=" stemming"> stemming</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space" title=" vector space"> vector space</a> </p> <a href="https://publications.waset.org/abstracts/184199/information-retrieval-for-kafficho-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184199.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">11815</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">11814</span> Enhanced Arabic Semantic Information Retrieval System Based on Arabic Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Elsehemy">A. Elsehemy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdeen"> M. Abdeen </a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Nazmy"> T. Nazmy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20text%20classification" title="Arabic text classification">Arabic text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20based%20retrieval" title=" ontology based retrieval"> ontology based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20semantic%20web" title=" Arabic semantic web"> Arabic semantic web</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=Arabic%20ontology" title=" Arabic ontology"> Arabic ontology</a> </p> <a href="https://publications.waset.org/abstracts/34945/enhanced-arabic-semantic-information-retrieval-system-based-on-arabic-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34945.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">525</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">11813</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.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">601</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">11812</span> Performance Evaluation of Content Based Image Retrieval Using Indexed Views </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Iqbal">Tahir Iqbal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ali"> Mumtaz Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Wajahat%20Kareem"> Syed Wajahat Kareem</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Harris"> Muhammad Harris </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital information is expanding in exponential order in our life. Information that is residing online and offline are stored in huge repositories relating to every aspect of our lives. Getting the required information is a task of retrieval systems. Content based image retrieval (CBIR) is a retrieval system that retrieves the required information from repositories on the basis of the contents of the image. Time is a critical factor in retrieval system and using indexed views with CBIR system improves the time efficiency of retrieved results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=content%20based%20image%20retrieval%20%28CBIR%29" title="content based image retrieval (CBIR)">content based image retrieval (CBIR)</a>, <a href="https://publications.waset.org/abstracts/search?q=indexed%20view" title=" indexed view"> indexed view</a>, <a href="https://publications.waset.org/abstracts/search?q=color" title=" color"> color</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20correlation" title=" cross correlation"> cross correlation</a> </p> <a href="https://publications.waset.org/abstracts/11165/performance-evaluation-of-content-based-image-retrieval-using-indexed-views" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11165.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">470</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">11811</span> Anatomical Survey for Text Pattern Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Tehsin">S. Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kausar"> S. Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20vision" title="biologically inspired vision">biologically inspired vision</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20retrieval" title=" content based retrieval"> content based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9629/anatomical-survey-for-text-pattern-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9629.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">444</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">11810</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">11809</span> Unlocking the Potential of Short Texts with Semantic Enrichment, Disambiguation Techniques, and Context Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mouheb%20Mehdoui">Mouheb Mehdoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20Fraisse"> Amel Fraisse</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the potential of short texts through semantic enrichment and disambiguation techniques. By employing context fusion, we aim to enhance the comprehension and utility of concise textual information. The methodologies utilized are grounded in recent advancements in natural language processing, which allow for a deeper understanding of semantics within limited text formats. Specifically, topic classification is employed to understand the context of the sentence and assess the relevance of added expressions. Additionally, word sense disambiguation is used to clarify unclear words, replacing them with more precise terms. The implications of this research extend to various applications, including information retrieval and knowledge representation. Ultimately, this work highlights the importance of refining short text processing techniques to unlock their full potential in real-world applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20traffic" title="information traffic">information traffic</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=word-sense%20disambiguation" title=" word-sense disambiguation"> word-sense disambiguation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20enrichment" title=" semantic enrichment"> semantic enrichment</a>, <a href="https://publications.waset.org/abstracts/search?q=ambiguity%20resolution" title=" ambiguity resolution"> ambiguity resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20enhancement" title=" short text enhancement"> short text enhancement</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=contextual%20understanding" title=" contextual understanding"> contextual understanding</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=ambiguity" title=" ambiguity"> ambiguity</a> </p> <a href="https://publications.waset.org/abstracts/193872/unlocking-the-potential-of-short-texts-with-semantic-enrichment-disambiguation-techniques-and-context-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193872.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">8</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">11808</span> Network Word Discovery Framework Based on Sentence Semantic Vector Similarity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ganfeng%20Yu">Ganfeng Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuefeng%20Ma"> Yuefeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Yang"> Shanliang Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The word discovery is a key problem in text information retrieval technology. Methods in new word discovery tend to be closely related to words because they generally obtain new word results by analyzing words. With the popularity of social networks, individual netizens and online self-media have generated various network texts for the convenience of online life, including network words that are far from standard Chinese expression. How detect network words is one of the important goals in the field of text information retrieval today. In this paper, we integrate the word embedding model and clustering methods to propose a network word discovery framework based on sentence semantic similarity (S³-NWD) to detect network words effectively from the corpus. This framework constructs sentence semantic vectors through a distributed representation model, uses the similarity of sentence semantic vectors to determine the semantic relationship between sentences, and finally realizes network word discovery by the meaning of semantic replacement between sentences. The experiment verifies that the framework not only completes the rapid discovery of network words but also realizes the standard word meaning of the discovery of network words, which reflects the effectiveness of our work. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20information%20retrieval" title="text information retrieval">text 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=new%20word%20discovery" title=" new word discovery"> new word discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a> </p> <a href="https://publications.waset.org/abstracts/153917/network-word-discovery-framework-based-on-sentence-semantic-vector-similarity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153917.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">95</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">11807</span> A Comparative Study of Approaches in User-Centred Health Information Retrieval</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harsh%20Thakkar">Harsh Thakkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ganesh%20Iyer"> Ganesh Iyer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we survey various user-centered or context-based biomedical health information retrieval systems. We present and discuss the performance of systems submitted in CLEF eHealth 2014 Task 3 for this purpose. We classify and focus on comparing the two most prevalent retrieval models in biomedical information retrieval namely: Language Model (LM) and Vector Space Model (VSM). We also report on the effectiveness of using external medical resources and ontologies like MeSH, Metamap, UMLS, etc. We observed that the LM based retrieval systems outperform VSM based systems on various fronts. From the results we conclude that the state-of-art system scores for MAP was 0.4146, P@10 was 0.7560 and NDCG@10 was 0.7445, respectively. All of these score were reported by systems built on language modeling approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20document%20retrieval" title="clinical document retrieval">clinical document retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=concept-based%20information%20retrieval" title=" concept-based information retrieval"> concept-based information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title=" query expansion"> query expansion</a>, <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=vector%20space%20models" title=" vector space models"> vector space models</a> </p> <a href="https://publications.waset.org/abstracts/57392/a-comparative-study-of-approaches-in-user-centred-health-information-retrieval" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57392.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">320</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">11806</span> Unsupervised Domain Adaptive Text Retrieval with Query Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Yin">Rui Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Haojie%20Wang"> Haojie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xun%20Li"> Xun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dense%20retrieval" title="dense retrieval">dense retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20generation" title=" query generation"> query generation</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20training" title=" unsupervised training"> unsupervised training</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20retrieval" title=" text retrieval"> text retrieval</a> </p> <a href="https://publications.waset.org/abstracts/173903/unsupervised-domain-adaptive-text-retrieval-with-query-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173903.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">73</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">11805</span> Comparison of Crossover Types to Obtain Optimal Queries Using Adaptive Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wafa%E2%80%99%20Alma%27Aitah">Wafa’ Alma'Aitah</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Almakadmeh"> Khaled Almakadmeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> this study presents an information retrieval system of using genetic algorithm to increase information retrieval efficiency. Using vector space model, information retrieval is based on the similarity measurement between query and documents. Documents with high similarity to query are judge more relevant to the query and should be retrieved first. Using genetic algorithms, each query is represented by a chromosome; these chromosomes are fed into genetic operator process: selection, crossover, and mutation until an optimized query chromosome is obtained for document retrieval. Results show that information retrieval with adaptive crossover probability and single point type crossover and roulette wheel as selection type give the highest recall. The proposed approach is verified using (242) proceedings abstracts collected from the Saudi Arabian national conference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</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=optimal%20queries" title=" optimal queries"> optimal queries</a>, <a href="https://publications.waset.org/abstracts/search?q=crossover" title=" crossover"> crossover</a> </p> <a href="https://publications.waset.org/abstracts/59109/comparison-of-crossover-types-to-obtain-optimal-queries-using-adaptive-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59109.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">292</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">11804</span> Searching Linguistic Synonyms through Parts of Speech Tagging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faiza%20Hussain">Faiza Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Usman%20Qamar"> Usman Qamar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synonym-based searching is recognized to be a complicated problem as text mining from unstructured data of web is challenging. Finding useful information which matches user need from bulk of web pages is a cumbersome task. In this paper, a novel and practical synonym retrieval technique is proposed for addressing this problem. For replacement of semantics, user intent is taken into consideration to realize the technique. Parts-of-Speech tagging is applied for pattern generation of the query and a thesaurus for this experiment was formed and used. Comparison with Non-Context Based Searching, Context Based searching proved to be a more efficient approach while dealing with linguistic semantics. This approach is very beneficial in doing intent based searching. Finally, results and future dimensions are presented. <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=text%20mining" title=" text mining"> text mining</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=parts-of-speech%20tagging" title=" parts-of-speech tagging"> parts-of-speech tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=grammar" title=" grammar"> grammar</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a> </p> <a href="https://publications.waset.org/abstracts/52077/searching-linguistic-synonyms-through-parts-of-speech-tagging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52077.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">307</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">11803</span> Retrieval-Induced Forgetting Effects in Retrospective and Prospective Memory in Normal Aging: An Experimental Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Merve%20Akca">Merve Akca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Retrieval-induced forgetting (RIF) refers to the phenomenon that selective retrieval of some information impairs memory for related, but not previously retrieved information. Despite age differences in retrieval-induced forgetting regarding retrospective memory being documented, this research aimed to highlight age differences in RIF of the prospective memory tasks for the first time. By using retrieval-practice paradigm, this study comparatively examined RIF effects in retrospective memory and event-based prospective memory in young and old adults. In this experimental study, a mixed factorial design with age group (Young, Old) as a between-subject variable, and memory type (Prospective, Retrospective) and item type (Practiced, Non-practiced) as within-subject variables was employed. Retrieval-induced forgetting was observed in the retrospective but not in the prospective memory task. Therefore, the results indicated that selective retrieval of past events led to suppression of other related past events in both age groups but not the suppression of memory for future intentions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prospective%20memory" title="prospective memory">prospective memory</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval-induced%20forgetting" title=" retrieval-induced forgetting"> retrieval-induced forgetting</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval%20inhibition" title=" retrieval inhibition"> retrieval inhibition</a>, <a href="https://publications.waset.org/abstracts/search?q=retrospective%20memory" title=" retrospective memory"> retrospective memory</a> </p> <a href="https://publications.waset.org/abstracts/57915/retrieval-induced-forgetting-effects-in-retrospective-and-prospective-memory-in-normal-aging-an-experimental-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57915.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">316</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">11802</span> Graph Codes - 2D Projections of Multimedia Feature Graphs for Fast and Effective Retrieval</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Wagenpfeil">Stefan Wagenpfeil</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20Engel"> Felix Engel</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20McKevitt"> Paul McKevitt</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthias%20Hemmje"> Matthias Hemmje</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multimedia Indexing and Retrieval is generally designed and implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results but also leads to more complex graph structures. However, graph-traversal-based algorithms for similarity are quite inefficient and computation intensive, especially for large data structures. To deliver fast and effective retrieval, an efficient similarity algorithm, particularly for large graphs, is mandatory. Hence, in this paper, we define a graph-projection into a 2D space (Graph Code) as well as the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph-traversals due to a simpler processing model and a high level of parallelization. In consequence, we prove that the effectiveness of retrieval also increases substantially, as Graph Codes facilitate more levels of detail in feature fusion. Thus, Graph Codes provide a significant increase in efficiency and effectiveness (especially for Multimedia indexing and retrieval) and can be applied to images, videos, audio, and text information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indexing" title="indexing">indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval" title=" retrieval"> retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=multimedia" title=" multimedia"> multimedia</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20algorithm" title=" graph algorithm"> graph algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20code" title=" graph code"> graph code</a> </p> <a href="https://publications.waset.org/abstracts/135289/graph-codes-2d-projections-of-multimedia-feature-graphs-for-fast-and-effective-retrieval" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135289.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">161</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">11801</span> Merging of Results in Distributed Information Retrieval Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Larbi%20Guezouli">Larbi Guezouli</a>, <a href="https://publications.waset.org/abstracts/search?q=Imane%20Azzouz"> Imane Azzouz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is located in the domain of distributed information retrieval ‘DIR’. A simplified view of the DIR requires a multi-search in a set of collections, which forces the system to analyze results found in these collections, and merge results back before sending them to the user in a single list. Our work is to find a fusion method based on the relevance score of each result received from collections and the relevance of the local search engine of each collection. <p class="card-text"><strong>Keywords:</strong> <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=distributed%20IR%20systems" title=" distributed IR systems"> distributed IR systems</a>, <a href="https://publications.waset.org/abstracts/search?q=merging%20results" title=" merging results"> merging results</a>, <a href="https://publications.waset.org/abstracts/search?q=datamining" title=" datamining"> datamining</a> </p> <a href="https://publications.waset.org/abstracts/37130/merging-of-results-in-distributed-information-retrieval-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37130.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">336</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">11800</span> Algorithm for Information Retrieval Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kehinde%20K.%20Agbele">Kehinde K. Agbele</a>, <a href="https://publications.waset.org/abstracts/search?q=Kehinde%20Daniel%20Aruleba"> Kehinde Daniel Aruleba</a>, <a href="https://publications.waset.org/abstracts/search?q=Eniafe%20F.%20Ayetiran"> Eniafe F. Ayetiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When using Information Retrieval Systems (IRS), users often present search queries made of ad-hoc keywords. It is then up to the IRS to obtain a precise representation of the user’s information need and the context of the information. This paper investigates optimization of IRS to individual information needs in order of relevance. The study addressed development of algorithms that optimize the ranking of documents retrieved from IRS. This study discusses and describes a Document Ranking Optimization (DROPT) algorithm for information retrieval (IR) in an Internet-based or designated databases environment. Conversely, as the volume of information available online and in designated databases is growing continuously, ranking algorithms can play a major role in the context of search results. In this paper, a DROPT technique for documents retrieved from a corpus is developed with respect to document index keywords and the query vectors. This is based on calculating the weight ( <p class="card-text"><strong>Keywords:</strong> <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=document%20relevance" title=" document relevance"> document relevance</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measures" title=" performance measures"> performance measures</a>, <a href="https://publications.waset.org/abstracts/search?q=personalization" title=" personalization"> personalization</a> </p> <a href="https://publications.waset.org/abstracts/40905/algorithm-for-information-retrieval-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40905.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">241</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">11799</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.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">100</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">11798</span> Biomedical Definition Extraction Using Machine Learning with Synonymous Feature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jian%20Qu">Jian Qu</a>, <a href="https://publications.waset.org/abstracts/search?q=Akira%20Shimazu"> Akira Shimazu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%. <p class="card-text"><strong>Keywords:</strong> <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=definition%20retrieval" title=" definition retrieval"> definition retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=OOV%20%28out%20of%20vocabulary%29" title=" OOV (out of vocabulary)"> OOV (out of vocabulary)</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20information%20retrieval" title=" biomedical information retrieval"> biomedical information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/39665/biomedical-definition-extraction-using-machine-learning-with-synonymous-feature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39665.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">495</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">11797</span> Morphological Processing of Punjabi Text for Sentiment Analysis of Farmer Suicides</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaspreet%20Singh">Jaspreet Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Gurvinder%20Singh"> Gurvinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Prabhsimran%20Singh"> Prabhsimran Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajinder%20Singh"> Rajinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Prithvipal%20Singh"> Prithvipal Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Karanjeet%20Singh%20Kahlon"> Karanjeet Singh Kahlon</a>, <a href="https://publications.waset.org/abstracts/search?q=Ravinder%20Singh%20Sawhney"> Ravinder Singh Sawhney</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title="deep neural network">deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=farmer%20suicides" title=" farmer suicides"> farmer suicides</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20processing" title=" morphological processing"> morphological processing</a>, <a href="https://publications.waset.org/abstracts/search?q=punjabi%20text" title=" punjabi text"> punjabi text</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/88605/morphological-processing-of-punjabi-text-for-sentiment-analysis-of-farmer-suicides" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88605.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">326</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">11796</span> Selection of Relevant Servers in Distributed Information Retrieval System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benhamouda%20Sara">Benhamouda Sara</a>, <a href="https://publications.waset.org/abstracts/search?q=Guezouli%20Larbi"> Guezouli Larbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, the dissemination of information touches the distributed world, where selecting the relevant servers to a user request is an important problem in distributed information retrieval. During the last decade, several research studies on this issue have been launched to find optimal solutions and many approaches of collection selection have been proposed. In this paper, we propose a new collection selection approach that takes into consideration the number of documents in a collection that contains terms of the query and the weights of those terms in these documents. We tested our method and our studies show that this technique can compete with other state-of-the-art algorithms that we choose to test the performance of our approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20information%20retrieval" title="distributed information retrieval">distributed information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=relevance" title=" relevance"> relevance</a>, <a href="https://publications.waset.org/abstracts/search?q=server%20selection" title=" server selection"> server selection</a>, <a href="https://publications.waset.org/abstracts/search?q=collection%20selection" title=" collection selection"> collection selection</a> </p> <a href="https://publications.waset.org/abstracts/37133/selection-of-relevant-servers-in-distributed-information-retrieval-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37133.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">312</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">11795</span> Enhanced Retrieval-Augmented Generation (RAG) Method with Knowledge Graph and Graph Neural Network (GNN) for Automated QA Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhihao%20Zheng">Zhihao Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhilin%20Wang"> Zhilin Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Linxin%20Liu"> Linxin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications. <p class="card-text"><strong>Keywords:</strong> <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=graph%20neural%20network" title=" graph neural network"> graph neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=retrieval-augmented%20generation" title=" retrieval-augmented generation"> retrieval-augmented generation</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a> </p> <a href="https://publications.waset.org/abstracts/188751/enhanced-retrieval-augmented-generation-rag-method-with-knowledge-graph-and-graph-neural-network-gnn-for-automated-qa-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188751.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">11794</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.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">518</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">11793</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">11792</span> Between AACR2 and RDA What Changes Occurs in Them</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Abdullahi%20Mohammad">Ibrahim Abdullahi Mohammad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A library catalogue exists not only as an inventory of the collections of the particular library, but also as a retrieval device. It is provided to assist the library user in finding whatever information or information resources they may be looking for. The paper proposes that this location objective of the library catalogue can only be fulfilled, if the library catalogue is constructed, bearing in mind the information needs and searching behavior of the library user. Comparing AACR2 and RDA viz-a-viz the changes RDA has introduced into bibliographic standards, the paper tries to establish the level of viability of RDA in relation to AACR2. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=library%20catalogue" title="library catalogue">library catalogue</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=AACR2" title=" AACR2"> AACR2</a>, <a href="https://publications.waset.org/abstracts/search?q=RDA" title=" RDA"> RDA</a> </p> <a href="https://publications.waset.org/abstracts/184325/between-aacr2-and-rda-what-changes-occurs-in-them" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184325.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">54</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">11791</span> The Acquisition of Case in Biological Domain Based on Text Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shen%20Jian">Shen Jian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu%20Jie"> Hu Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Qi%20Jin"> Qi Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Wei%20Jie"> Liu Wei Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen%20Ji%20Yi"> Chen Ji Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Ying%20Hong"> Peng Ying Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to settle the problem of acquiring case in biological related to design problems, a biometrics instance acquisition method based on text mining is presented. Through the construction of corpus text vector space and knowledge mining, the feature selection, similarity measure and case retrieval method of text in the field of biology are studied. First, we establish a vector space model of the corpus in the biological field and complete the preprocessing steps. Then, the corpus is retrieved by using the vector space model combined with the functional keywords to obtain the biological domain examples related to the design problems. Finally, we verify the validity of this method by taking the example of text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20space%20model" title=" vector space model"> vector space model</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20design" title=" biologically inspired design"> biologically inspired design</a> </p> <a href="https://publications.waset.org/abstracts/88075/the-acquisition-of-case-in-biological-domain-based-on-text-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88075.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">11790</span> Secure Image Retrieval Based on Orthogonal Decomposition under Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Xu">Y. Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Xiong"> L. Xiong</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Xu"> Z. Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to protect data privacy, image with sensitive or private information needs to be encrypted before being outsourced to the cloud. However, this causes difficulties in image retrieval and data management. A secure image retrieval method based on orthogonal decomposition is proposed in the paper. The image is divided into two different components, for which encryption and feature extraction are executed separately. As a result, cloud server can extract features from an encrypted image directly and compare them with the features of the queried images, so that the user can thus obtain the image. Different from other methods, the proposed method has no special requirements to encryption algorithms. Experimental results prove that the proposed method can achieve better security and better retrieval precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=secure%20image%20retrieval" title="secure image retrieval">secure image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20search" title=" secure search"> secure search</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20decomposition" title=" orthogonal decomposition"> orthogonal decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20cloud%20computing" title=" secure cloud computing"> secure cloud computing</a> </p> <a href="https://publications.waset.org/abstracts/29115/secure-image-retrieval-based-on-orthogonal-decomposition-under-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29115.pdf" target="_blank" class="btn 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