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enhancement</title> <meta name="description" content="Search results for: short text enhancement"> <meta name="keywords" content="short text enhancement"> <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 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<div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5554</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">9</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">5553</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.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">126</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">5552</span> Experimental Study of Hyperparameter Tuning a Deep Learning Convolutional Recurrent Network for Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bharatendra%20Rai">Bharatendra Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20networks" title="long short-term memory networks">long short-term memory networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20recurrent%20networks" title=" convolutional recurrent networks"> convolutional recurrent networks</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=hyperparameter%20tuning" title=" hyperparameter tuning"> hyperparameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Tukey%20honest%20significant%20differences" title=" Tukey honest significant differences"> Tukey honest significant differences</a> </p> <a href="https://publications.waset.org/abstracts/169795/experimental-study-of-hyperparameter-tuning-a-deep-learning-convolutional-recurrent-network-for-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169795.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">129</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">5551</span> Programmed Speech to Text Summarization Using Graph-Based Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamsini%20Pulugurtha">Hamsini Pulugurtha</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20S.%20L.%20Jagadamba"> P. V. S. L. Jagadamba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20speech" title=" English speech"> English speech</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20text" title=" English text"> English text</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=unsupervised%20extractive%20text%20summarization" title=" unsupervised extractive text summarization"> unsupervised extractive text summarization</a> </p> <a href="https://publications.waset.org/abstracts/143079/programmed-speech-to-text-summarization-using-graph-based-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143079.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">219</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">5550</span> Short Text Classification for Saudi Tweets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20A.%20Alsufyani">Asma A. Alsufyani</a>, <a href="https://publications.waset.org/abstracts/search?q=Maram%20A.%20Alharthi"> Maram A. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maha%20J.%20Althobaiti"> Maha J. Althobaiti</a>, <a href="https://publications.waset.org/abstracts/search?q=Manal%20S.%20Alharthi"> Manal S. Alharthi</a>, <a href="https://publications.waset.org/abstracts/search?q=Huda%20Rizq"> Huda Rizq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20creation" title="corpus creation">corpus creation</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20text%20classification" title=" short text classification"> short text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/130952/short-text-classification-for-saudi-tweets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130952.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">155</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">5549</span> Comparative Methods for Speech Enhancement and the Effects on Text-Independent Speaker Identification Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Ajgou">R. Ajgou</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Sbaa"> S. Sbaa</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghendir"> S. Ghendir</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chemsa"> A. Chemsa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Taleb-Ahmed"> A. Taleb-Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The speech enhancement algorithm is to improve speech quality. In this paper, we review some speech enhancement methods and we evaluated their performance based on Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862). All method was evaluated in presence of different kind of noise using TIMIT database and NOIZEUS noisy speech corpus.. The noise was taken from the AURORA database and includes suburban train noise, babble, car, exhibition hall, restaurant, street, airport and train station noise. Simulation results showed improved performance of speech enhancement for Tracking of non-stationary noise approach in comparison with various methods in terms of PESQ measure. Moreover, we have evaluated the effects of the speech enhancement technique on Speaker Identification system based on autoregressive (AR) model and Mel-frequency Cepstral coefficients (MFCC). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20enhancement" title="speech enhancement">speech enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=pesq" title=" pesq"> pesq</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a> </p> <a href="https://publications.waset.org/abstracts/31102/comparative-methods-for-speech-enhancement-and-the-effects-on-text-independent-speaker-identification-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31102.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">424</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">5548</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</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=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.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">71</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">5547</span> Adaptation in Translation of 'Christmas Every Day' Short Story by William Dean Howells</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsine%20Khazrouni">Mohsine Khazrouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study is an attempt to highlight the importance of adaptation in translation. To convey the message, the translator needs to take into account not only the text but also extra-linguistic factors such as the target audience. The present paper claims that adaptation is an unavoidable translation strategy when dealing with texts that are heavy with religious and cultural themes. The translation task becomes even more challenging when dealing with children’s literature as the audience are children whose comprehension, experience and world knowledge are limited. The study uses the Arabic translation of the short story ‘Christmas Every Day’ as a case study. The short story will be translated, and the pragmatic problems involved will be discussed. The focus will be on the issue of adaptation. i.e., the source text should be adapted to the target language audience`s social and cultural environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pragmatic%20adaptation" title="pragmatic adaptation">pragmatic adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20translation" title=" Arabic translation"> Arabic translation</a>, <a href="https://publications.waset.org/abstracts/search?q=children%27s%20literature" title=" children's literature"> children's literature</a>, <a href="https://publications.waset.org/abstracts/search?q=equivalence" title=" equivalence"> equivalence</a> </p> <a href="https://publications.waset.org/abstracts/55087/adaptation-in-translation-of-christmas-every-day-short-story-by-william-dean-howells" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55087.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">215</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">5546</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">5545</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">5544</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">5543</span> N400 Investigation of Semantic Priming Effect to Symbolic Pictures in Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Ousterhout">Thomas Ousterhout</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study was to investigate if incorporating meaningful pictures of gestures and facial expressions in short sentences of text could supplement the text with enough semantic information to produce and N400 effect when probe words incongruent to the picture were subsequently presented. Event-related potentials (ERPs) were recorded from a 14-channel commercial grade EEG headset while subjects performed congruent/incongruent reaction time discrimination tasks. Since pictures of meaningful gestures have been shown to be semantically processed in the brain in a similar manner as words are, it is believed that pictures will add supplementary information to text just as the inclusion of their equivalent synonymous word would. The hypothesis is that when subjects read the text/picture mixed sentences, they will process the images and words just like in face-to-face communication and therefore probe words incongruent to the image will produce an N400. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ERP" title=" ERP"> ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=N400" title=" N400"> N400</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=congruency" title=" congruency"> congruency</a>, <a href="https://publications.waset.org/abstracts/search?q=facilitation" title=" facilitation"> facilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=Emotiv" title=" Emotiv"> Emotiv</a> </p> <a href="https://publications.waset.org/abstracts/48705/n400-investigation-of-semantic-priming-effect-to-symbolic-pictures-in-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48705.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">258</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">5542</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">5541</span> The Morphology of Sri Lankan Text Messages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chamindi%20Dilkushi%20Senaratne">Chamindi Dilkushi Senaratne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communicating via a text or an SMS (Short Message Service) has become an integral part of our daily lives. With the increase in the use of mobile phones, text messaging has become a genre by itself worth researching and studying. It is undoubtedly a major phenomenon revealing language change. This paper attempts to describe the morphological processes of text language of urban bilinguals in Sri Lanka. It will be a typological study based on 500 English text messages collected from urban bilinguals residing in Colombo. The messages are selected by categorizing the deviant forms of language use apparent in text messages. These stylistic deviations are a deliberate skilled performance by the users of the language possessing an in-depth knowledge of linguistic systems to create new words and thereby convey their linguistic identity and individual and group solidarity via the message. The findings of the study solidifies arguments that the manipulation of language in text messages is both creative and appropriate. In addition, code mixing theories will be used to identify how existing morphological processes are adapted by bilingual users in Sri Lanka when texting. The study will reveal processes such as omission, initialism, insertion and alternation in addition to other identified linguistic features in text language. The corpus reveals the most common morphological processes used by Sri Lankan urban bilinguals when sending texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bilingual" title="bilingual">bilingual</a>, <a href="https://publications.waset.org/abstracts/search?q=deviations" title=" deviations"> deviations</a>, <a href="https://publications.waset.org/abstracts/search?q=morphology" title=" morphology"> morphology</a>, <a href="https://publications.waset.org/abstracts/search?q=texts" title=" texts"> texts</a> </p> <a href="https://publications.waset.org/abstracts/57197/the-morphology-of-sri-lankan-text-messages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57197.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">269</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">5540</span> “A Built-In, Shockproof, Shit Detector”: Major Challenges and Peculiarities of Translating Ernest Hemingway’s Short Stories Into Georgian</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natia%20Kvachakidze">Natia Kvachakidze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Translating fiction is a complicated and multidimensional issue. However, studying and analyzing literary translations is not less challenging. This becomes even more complex due to the existence of several alternative translations of one and the same literary work. However, this also makes the research process more interesting at the same time. The aim of the given work is to distinguish major obstacles and challenges translators come across while working on Ernest Hemingway’s short fiction, as well as to analyze certain peculiarities and characteristic features of some existing Georgian translations of the writer’s work (especially in the context of various alternative versions of some well-known short stories). Consequently, the focus is on studying how close these translations come to the form and the context of the original text in order to see if the linguistic and stylistic characteristics of the original author are preserved. Moreover, it is interesting not only to study the relevance of each translation to the original text but also to present a comparative analysis of some major peculiarities of the given translations, which are naturally characterized by certain strengths and weaknesses. The latter is at times inevitable, but in certain cases, there is room for improvement. The given work also attempts to humbly suggest certain ways of possible improvements of some translation inadequacies, as this can provide even more opportunities for deeper and detailed studies in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hemingway" title="Hemingway">Hemingway</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20fiction" title=" short fiction"> short fiction</a>, <a href="https://publications.waset.org/abstracts/search?q=translation" title=" translation"> translation</a>, <a href="https://publications.waset.org/abstracts/search?q=Georgian" title=" Georgian"> Georgian</a> </p> <a href="https://publications.waset.org/abstracts/154115/a-built-in-shockproof-shit-detector-major-challenges-and-peculiarities-of-translating-ernest-hemingways-short-stories-into-georgian" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154115.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">88</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">5539</span> An Event Relationship Extraction Method Incorporating Deep Feedback Recurrent Neural Network and Bidirectional Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yin%20Yuanling">Yin Yuanling</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=event%20relations" title="event relations">event relations</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=DFRNN%20models" title=" DFRNN models"> DFRNN models</a>, <a href="https://publications.waset.org/abstracts/search?q=bi-directional%20long%20and%20short-term%20memory%20networks" title=" bi-directional long and short-term memory networks"> bi-directional long and short-term memory networks</a> </p> <a href="https://publications.waset.org/abstracts/156673/an-event-relationship-extraction-method-incorporating-deep-feedback-recurrent-neural-network-and-bidirectional-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156673.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">144</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">5538</span> Improving Depression Symptoms and Antidepressant Medication Adherence Using Encrypted Short Message Service Text Message Reminders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ogbonna%20Olelewe">Ogbonna Olelewe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This quality improvement project seeks to address the background and significance of promoting antidepressant (AD) medication adherence to reduce depression symptoms in patients diagnosed with major depression. This project aims to substantiate using daily encrypted short message service (SMS) text reminders to take prescribed antidepressant medications with the goal of increasing medication adherence to reduce depression scores in patients diagnosed with major depression, thereby preventing relapses and increasing remission rates. Depression symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) scale. The PHQ-9 provides a total score of depression symptoms from mild to severe, ranging from 0 to 27. A -pretest/post-test design was used, with a convenience sample size of 35 adult patients aged 18 years old to 45 years old, diagnosed with MDD, and prescribed at least one antidepressant for one year or more. Pre- and post-test PHQ-9 scores were conducted to compare depression scores before and after the four-week intervention period. The results indicated improved post-intervention PHQ-9 scores, improved AD medication adherence, and a significant reduction in depression symptoms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=major%20depressive%20disorder" title="major depressive disorder">major depressive disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=antidepressants" title=" antidepressants"> antidepressants</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20message%20services" title=" short message services"> short message services</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20reminders" title=" text reminders"> text reminders</a>, <a href="https://publications.waset.org/abstracts/search?q=Medication%20adherence%2Fnon-adherence" title=" Medication adherence/non-adherence"> Medication adherence/non-adherence</a>, <a href="https://publications.waset.org/abstracts/search?q=Patient%20Health%20Questionnaire%209" title=" Patient Health Questionnaire 9"> Patient Health Questionnaire 9</a> </p> <a href="https://publications.waset.org/abstracts/138394/improving-depression-symptoms-and-antidepressant-medication-adherence-using-encrypted-short-message-service-text-message-reminders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138394.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">152</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">5537</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.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">168</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">5536</span> Information Extraction for Short-Answer Question for the University of the Cordilleras</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thelma%20Palaoag">Thelma Palaoag</a>, <a href="https://publications.waset.org/abstracts/search?q=Melanie%20Basa"> Melanie Basa</a>, <a href="https://publications.waset.org/abstracts/search?q=Jezreel%20Mark%20Panilo"> Jezreel Mark Panilo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Checking short-answer questions and essays, whether it may be paper or electronic in form, is a tiring and tedious task for teachers. Evaluating a student’s output require wide array of domains. Scoring the work is often a critical task. Several attempts in the past few years to create an automated writing assessment software but only have received negative results from teachers and students alike due to unreliability in scoring, does not provide feedback and others. The study aims to create an application that will be able to check short-answer questions which incorporate information extraction. Information extraction is a subfield of Natural Language Processing (NLP) where a chunk of text (technically known as unstructured text) is being broken down to gather necessary bits of data and/or keywords (structured text) to be further analyzed or rather be utilized by query tools. The proposed system shall be able to extract keywords or phrases from the individual’s answers to match it into a corpora of words (as defined by the instructor), which shall be the basis of evaluation of the individual’s answer. The proposed system shall also enable the teacher to provide feedback and re-evaluate the output of the student for some writing elements in which the computer cannot fully evaluate such as creativity and logic. Teachers can formulate, design, and check short answer questions efficiently by defining keywords or phrases as parameters by assigning weights for checking answers. With the proposed system, teacher’s time in checking and evaluating students output shall be lessened, thus, making the teacher more productive and easier. <p class="card-text"><strong>Keywords:</strong> <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=short-answer%20question" title=" short-answer question"> short-answer question</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=application" title=" application"> application</a> </p> <a href="https://publications.waset.org/abstracts/62355/information-extraction-for-short-answer-question-for-the-university-of-the-cordilleras" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62355.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">428</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">5535</span> On-Road Text Detection Platform for Driver Assistance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guezouli%20Larbi">Guezouli Larbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Belkacem%20Soundes"> Belkacem Soundes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=PZM" title=" PZM"> PZM</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/161507/on-road-text-detection-platform-for-driver-assistance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161507.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">83</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">5534</span> Reducing Accidents Using Text Stops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benish%20Chaudhry">Benish Chaudhry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the accidents these days are occurring because of the ‘text-and-drive’ concept. If we look at the structure of cities in UAE, there are great distances, because of which it is impossible to drive without using or merely checking the cellphone. Moreover, if we look at the road structure, it is almost impossible to stop at a point and text. With the introduction of TEXT STOPs, drivers will be able to stop different stops for a maximum of 1 and a half-minute in order to reply or write a message. They can be introduced at a distance of 10 minutes of driving on the average speed of the road, so the drivers can look forward to a stop and can reply to a text when needed. A user survey indicates that drivers are willing to NOT text-and-drive if they have such a facility available. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transport" title="transport">transport</a>, <a href="https://publications.waset.org/abstracts/search?q=accidents" title=" accidents"> accidents</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20planning" title=" urban planning"> urban planning</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20planning" title=" road planning"> road planning</a> </p> <a href="https://publications.waset.org/abstracts/44563/reducing-accidents-using-text-stops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44563.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">394</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">5533</span> A Critical Discourse Study of Gender Identity Issues in Daniyal Mueenuddin’s Short Story “Saleema”</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zafar%20Ali">Zafar Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to highlight problems that are faced by women at the hands of men. Males in Pakistani society have power and use this power for the exploitation of women. Further, the purpose of the study is to make societies like Pakistan and especially the young generation, aware and enable them to resist such issues, and the role of discourse in this regard is to minimize its political and social repercussions. The study finds out different discursive techniques and manipulative language used in the short story to construct gender identity. The study also investigates socio-economic roles in the construction of gender identity. This study has been completed with the help of Critical Discourse Analysis (CDA) principles. CDA principles have been applied to the text of the selected short story Saleema from Daniyal Mueenuddin’s collection In Other Rooms, Other Wonders. Related passages, structures, expressions, and text are analyzed from the point of view of CDA, especially Norman Fairclough’s CDA approach. It was found from the analysis that women have no identity of their own in patriarchal societies like Pakistan. Further, it was found women are mistreated, and they have a very limited and defined role in Pakistan. They cannot go beyond the limit defined to them by men. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20issues" title="gender issues">gender issues</a>, <a href="https://publications.waset.org/abstracts/search?q=resourceful%20groups" title=" resourceful groups"> resourceful groups</a>, <a href="https://publications.waset.org/abstracts/search?q=CDA" title=" CDA"> CDA</a>, <a href="https://publications.waset.org/abstracts/search?q=exploitation" title=" exploitation"> exploitation</a> </p> <a href="https://publications.waset.org/abstracts/153547/a-critical-discourse-study-of-gender-identity-issues-in-daniyal-mueenuddins-short-story-saleema" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153547.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">131</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">5532</span> Structure Analysis of Text-Image Connection in Jalayrid Period Illustrated Manuscripts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Khani%20Oushani">Mahsa Khani Oushani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text and image are two important elements in the field of Iranian art, the text component and the image component have always been manifested together. The image narrates the text and the text is the factor in the formation of the image and they are closely related to each other. The connection between text and image is an interactive and two-way connection in the tradition of Iranian manuscript arrangement. The interaction between the narrative description and the image scene is the result of a direct and close connection between the text and the image, which in addition to the decorative aspect, also has a descriptive aspect. In this article the connection between the text element and the image element and its adaptation to the theory of Roland Barthes, the structuralism theorist, in this regard will be discussed. This study tends to investigate the question of how the connection between text and image in illustrated manuscripts of the Jalayrid period is defined according to Barthes’ theory. And what kind of proportion has the artist created in the composition between text and image. Based on the results of reviewing the data of this study, it can be inferred that in the Jalayrid period, the image has a reference connection and although it is of major importance on the page, it also maintains a close connection with the text and is placed in a special proportion. It is not necessarily balanced and symmetrical and sometimes uses imbalance for composition. This research has been done by descriptive-analytical method, which has been done by library collection method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structure" title="structure">structure</a>, <a href="https://publications.waset.org/abstracts/search?q=text" title=" text"> text</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalayrid" title=" Jalayrid"> Jalayrid</a>, <a href="https://publications.waset.org/abstracts/search?q=painter" title=" painter"> painter</a> </p> <a href="https://publications.waset.org/abstracts/138869/structure-analysis-of-text-image-connection-in-jalayrid-period-illustrated-manuscripts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138869.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">234</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">5531</span> Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faisal%20Alshuwaier">Faisal Alshuwaier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Areshey"> Ali Areshey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text mining techniques are generally applied for classifying the text, finding fuzzy relations and structures in data sets. This research provides plenty text mining capabilities. One common application is text classification and event extraction, which encompass deducing specific knowledge concerning incidents referred to in texts. The main contribution of this paper is the clarification of a concept graph generation mechanism, which is based on a text classification and optimal fuzzy relationship extraction. Furthermore, the work presented in this paper explains the application of fuzzy relationship extraction and branch and bound method to simplify the texts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extraction" title="extraction">extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=max-prod" title=" max-prod"> max-prod</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20relations" title=" fuzzy relations"> fuzzy relations</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=memberships" title=" memberships"> memberships</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=memberships" title=" memberships"> memberships</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/23970/optimal-classifying-and-extracting-fuzzy-relationship-from-query-using-text-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23970.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">582</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">5530</span> A Method for Compression of Short Unicode Strings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Abedi">Masoud Abedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbas%20Malekpour"> Abbas Malekpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20Luksch"> Peter Luksch</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Reza%20Mojtabaei"> Mohammad Reza Mojtabaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of short texts in communication has been greatly increasing in recent years. Applying different languages in short texts has led to compulsory use of Unicode strings. These strings need twice the space of common strings, hence, applying algorithms of compression for the purpose of accelerating transmission and reducing cost is worthwhile. Nevertheless, other compression methods like gzip, bzip2 or PAQ due to high overhead data size are not appropriate. The Huffman algorithm is one of the rare algorithms effective in reducing the size of short Unicode strings. In this paper, an algorithm is proposed for compression of very short Unicode strings. At first, every new character to be sent to a destination is inserted in the proposed mapping table. At the beginning, every character is new. In case the character is repeated for the same destination, it is not considered as a new character. Next, the new characters together with the mapping value of repeated characters are arranged through a specific technique and specially formatted to be transmitted. The results obtained from an assessment made on a set of short Persian and Arabic strings indicate that this proposed algorithm outperforms the Huffman algorithm in size reduction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Algorithms" title="Algorithms">Algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Data%20Compression" title=" Data Compression"> Data Compression</a>, <a href="https://publications.waset.org/abstracts/search?q=Decoding" title=" Decoding"> Decoding</a>, <a href="https://publications.waset.org/abstracts/search?q=Encoding" title=" Encoding"> Encoding</a>, <a href="https://publications.waset.org/abstracts/search?q=Huffman%20Codes" title=" Huffman Codes"> Huffman Codes</a>, <a href="https://publications.waset.org/abstracts/search?q=Text%20Communication" title=" Text Communication"> Text Communication</a> </p> <a href="https://publications.waset.org/abstracts/66703/a-method-for-compression-of-short-unicode-strings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66703.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">348</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">5529</span> Mask-Prompt-Rerank: An Unsupervised Method for Text Sentiment Transfer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yufen%20Qin">Yufen Qin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text sentiment transfer is an important branch of text style transfer. The goal is to generate text with another sentiment attribute based on a text with a specific sentiment attribute while maintaining the content and semantic information unrelated to sentiment unchanged in the process. There are currently two main challenges in this field: no parallel corpus and text attribute entanglement. In response to the above problems, this paper proposed a novel solution: Mask-Prompt-Rerank. Use the method of masking the sentiment words and then using prompt regeneration to transfer the sentence sentiment. Experiments on two sentiment benchmark datasets and one formality transfer benchmark dataset show that this approach makes the performance of small pre-trained language models comparable to that of the most advanced large models, while consuming two orders of magnitude less computing and memory. <p class="card-text"><strong>Keywords:</strong> <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=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=prompt" title=" prompt"> prompt</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20sentiment%20transfer" title=" text sentiment transfer"> text sentiment transfer</a> </p> <a href="https://publications.waset.org/abstracts/173904/mask-prompt-rerank-an-unsupervised-method-for-text-sentiment-transfer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173904.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">81</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">5528</span> Exploratory Analysis of A Review of Nonexistence Polarity in Native Speech</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deawan%20Rakin%20Ahamed%20Remal">Deawan Rakin Ahamed Remal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinthia%20Chowdhury"> Sinthia Chowdhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharun%20Akter%20Khushbu"> Sharun Akter Khushbu</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheak%20Rashed%20Haider%20Noori"> Sheak Rashed Haider Noori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Native Speech to text synthesis has its own leverage for the purpose of mankind. The extensive nature of art to speaking different accents is common but the purpose of communication between two different accent types of people is quite difficult. This problem will be motivated by the extraction of the wrong perception of language meaning. Thus, many existing automatic speech recognition has been placed to detect text. Overall study of this paper mentions a review of NSTTR (Native Speech Text to Text Recognition) synthesis compared with Text to Text recognition. Review has exposed many text to text recognition systems that are at a very early stage to comply with the system by native speech recognition. Many discussions started about the progression of chatbots, linguistic theory another is rule based approach. In the Recent years Deep learning is an overwhelming chapter for text to text learning to detect language nature. To the best of our knowledge, In the sub continent a huge number of people speak in Bangla language but they have different accents in different regions therefore study has been elaborate contradictory discussion achievement of existing works and findings of future needs in Bangla language acoustic accent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TTR" title="TTR">TTR</a>, <a href="https://publications.waset.org/abstracts/search?q=NSTTR" title=" NSTTR"> NSTTR</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20to%20text%20recognition" title=" text to text recognition"> text to text recognition</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=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/149060/exploratory-analysis-of-a-review-of-nonexistence-polarity-in-native-speech" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149060.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">132</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">5527</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">5526</span> The Arabic Literary Text, between Proficiency and Pedagogy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahman%20M.%20Chamseddine">Abdul Rahman M. Chamseddine</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20El-ashiri"> Mahmoud El-ashiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of language teaching, communication skills are essential for the learner to achieve, however, these skills, in general, might not support the comprehension of some texts of literary or artistic nature like poetry. Understanding sentences and expressions is not enough to understand a poem; other skills are needed in order to understand the special structure of a text which literary meaning is inapprehensible even when the lingual meaning is well comprehended. And then there is the need for many other components that surpass one text to other similar texts that can be understood through solid traditions, which do not form an obstacle in the face of change and progress. This is not exclusive to texts that are classified as a literary but it is also the same with some daily short phrases and indicatively charged expressions that can be classified as literary or bear a taste of literary nature.. it can be found in Newpapers’ titles, TV news reports, and maybe football commentaries… the need to understand this special lingual use – described as literary – is highly important to understand this discourse that can be generally classified as very far from literature. This work will try to explore the role of the literary text in the language class and the way it is being covered or dealt with throughout all levels of acquiring proficiency. It will also attempt to survery the position of the literary text in some of the most important books for teaching Arabic around the world. The same way grammar is needed to understand the language, another (literary) grammar is also needed for understanding literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20teaching" title="language teaching">language teaching</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=literature" title=" literature"> literature</a>, <a href="https://publications.waset.org/abstracts/search?q=pedagogy" title=" pedagogy"> pedagogy</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20proficiency" title=" language proficiency "> language proficiency </a> </p> <a href="https://publications.waset.org/abstracts/40359/the-arabic-literary-text-between-proficiency-and-pedagogy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40359.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">272</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">5525</span> Arabic Text Representation and Classification Methods: Current State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rami%20Ayadi">Rami Ayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui"> Mohsen Maraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have presented a brief current state of the art for Arabic text representation and classification methods. We decomposed Arabic Task Classification into four categories. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC. <p class="card-text"><strong>Keywords:</strong> <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=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=impact%20of%20preprocessing" title=" impact of preprocessing"> impact of preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a> </p> <a href="https://publications.waset.org/abstracts/10277/arabic-text-representation-and-classification-methods-current-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10277.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">469</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=short%20text%20enhancement&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=short%20text%20enhancement&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=short%20text%20enhancement&page=4">4</a></li> <li class="page-item"><a class="page-link" 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