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word2vec</title> <meta name="description" content="Search results for: word2vec"> <meta name="keywords" content="word2vec"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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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="word2vec"> <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> 12</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: word2vec</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12</span> Resume Ranking Using Custom Word2vec and Rule-Based Natural Language Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subodh%20Chandra%20Shakya">Subodh Chandra Shakya</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Sapkota"> Rajendra Sapkota</a>, <a href="https://publications.waset.org/abstracts/search?q=Aakash%20Tamang"> Aakash Tamang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shushant%20Pudasaini"> Shushant Pudasaini</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujan%20Adhikari"> Sujan Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajjan%20Adhikari"> Sajjan Adhikari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chunking" title="chunking">chunking</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20similarity" title=" document similarity"> document similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20extraction" title=" information extraction"> information extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embedding" title=" word embedding"> word embedding</a> </p> <a href="https://publications.waset.org/abstracts/129534/resume-ranking-using-custom-word2vec-and-rule-based-natural-language-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11</span> Online Topic Model for Broadcasting Contents Using Semantic Correlation Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang-Uk%20Kwak">Chang-Uk Kwak</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun-Joong%20Kim"> Sun-Joong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong-Bae%20Park"> Seong-Bae Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang-Jo%20Lee"> Sang-Jo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a method of learning topics for broadcasting contents. There are two kinds of texts related to broadcasting contents. One is a broadcasting script which is a series of texts including directions and dialogues. The other is blogposts which possesses relatively abstracted contents, stories and diverse information of broadcasting contents. Although two texts range over similar broadcasting contents, words in blogposts and broadcasting script are different. In order to improve the quality of topics, it needs a method to consider the word difference. In this paper, we introduce a semantic vocabulary expansion method to solve the word difference. We expand topics of the broadcasting script by incorporating the words in blogposts. Each word in blogposts is added to the most semantically correlated topics. We use word2vec to get the semantic correlation between words in blogposts and topics of scripts. The vocabularies of topics are updated and then posterior inference is performed to rearrange the topics. In experiments, we verified that the proposed method can learn more salient topics for broadcasting contents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=broadcasting%20script%20analysis" title="broadcasting script analysis">broadcasting script analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20expansion" title=" topic expansion"> topic expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20correlation%20analysis" title=" semantic correlation analysis"> semantic correlation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a> </p> <a href="https://publications.waset.org/abstracts/43213/online-topic-model-for-broadcasting-contents-using-semantic-correlation-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43213.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">251</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">10</span> Genomic Sequence Representation Learning: An Analysis of K-Mer Vector Embedding Dimensionality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=James%20Jr.%20Mashiyane">James Jr. Mashiyane</a>, <a href="https://publications.waset.org/abstracts/search?q=Risuna%20Nkolele"> Risuna Nkolele</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephanie%20J.%20M%C3%BCller"> Stephanie J. Müller</a>, <a href="https://publications.waset.org/abstracts/search?q=Gciniwe%20S.%20Dlamini"> Gciniwe S. Dlamini</a>, <a href="https://publications.waset.org/abstracts/search?q=Rebone%20L.%20Meraba"> Rebone L. Meraba</a>, <a href="https://publications.waset.org/abstracts/search?q=Darlington%20S.%20Mapiye"> Darlington S. Mapiye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=word%20embeddings" title="word embeddings">word embeddings</a>, <a href="https://publications.waset.org/abstracts/search?q=k-mer%20embedding" title=" k-mer embedding"> k-mer embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%0D%0Areduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/151370/genomic-sequence-representation-learning-an-analysis-of-k-mer-vector-embedding-dimensionality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151370.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">137</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">9</span> Text Emotion Recognition by Multi-Head Attention based Bidirectional LSTM Utilizing Multi-Level Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwanath%20Pethri%20Kamath">Vishwanath Pethri Kamath</a>, <a href="https://publications.waset.org/abstracts/search?q=Jayantha%20Gowda%20Sarapanahalli"> Jayantha Gowda Sarapanahalli</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Mishra"> Vishal Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Siddhesh%20Balwant%20Bandgar"> Siddhesh Balwant Bandgar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of emotional information is essential in any form of communication. Growing HCI (Human-Computer Interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The text being the least expressive amongst the multimodal resources poses various challenges such as contextual information and also sequential nature of the language construction. In this research work, the proposal is made for a neural architecture to resolve not less than 8 emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a Multi-head attention-based bidirectional LSTM model with a one-vs-all Multi-Level Classification. The emotions targeted in this research are Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame, and Surprise. Textual data from multiple datasets were used for this research work such as ISEAR, Go Emotions, Affect datasets for creating the emotions’ dataset. Data samples overlap or conflicts were considered with careful preprocessing. Our results show a significant improvement with the modeling architecture and as good as 10 points improvement in recognizing some emotions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20emotion%20recognition" title="text emotion recognition">text emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20LSTM" title=" bidirectional LSTM"> bidirectional LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-head%20attention" title=" multi-head attention"> multi-head attention</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-level%20classification" title=" multi-level classification"> multi-level classification</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20word2vec%20word%20embeddings" title=" google word2vec word embeddings"> google word2vec word embeddings</a> </p> <a href="https://publications.waset.org/abstracts/148957/text-emotion-recognition-by-multi-head-attention-based-bidirectional-lstm-utilizing-multi-level-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148957.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">174</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">8</span> Automatic Lexicon Generation for Domain Specific Dataset for Mining Public Opinion on China Pakistan Economic Corridor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tayyaba%20Azim">Tayyaba Azim</a>, <a href="https://publications.waset.org/abstracts/search?q=Bibi%20Amina"> Bibi Amina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet. <p class="card-text"><strong>Keywords:</strong> <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=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</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=Word2vec" title=" Word2vec"> Word2vec</a> </p> <a href="https://publications.waset.org/abstracts/104963/automatic-lexicon-generation-for-domain-specific-dataset-for-mining-public-opinion-on-china-pakistan-economic-corridor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104963.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">148</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">7</span> The Evolution of Moral Politics: Analysis on Moral Foundations of Korean Parties </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Changdong%20Oh">Changdong Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the arrival of post-industrial society, social scientists have been giving attention to issues of which factors shape cleavage of political parties. Especially, there is a heated controversy over whether and how social and cultural values influence the identities of parties and voting behavior. Drawing from Moral Foundations Theory (MFT), which approached similar issues by considering the effect of five moral foundations on political decision-making of people, this study investigates the role of moral rhetoric in the evolution of Korean political parties. Researcher collected official announcements released by the major two parties (Democratic Party of Korea, Saenuri Party) from 2007 to 2016, and analyzed the data by using Word2Vec algorithm and Moral Foundations Dictionary. Five moral decision modules of MFT, composed of care, fairness (individualistic morality), loyalty, authority and sanctity (group-based, Durkheimian morality), can be represented in vector spaces consisted of party announcements data. By comparing the party vector and the five morality vectors, researcher can see how the political parties have actively used each of the five moral foundations to express themselves and the opposition. Results report that the conservative party tends to actively draw on collective morality such as loyalty, authority, purity to differentiate itself. Notably, such moral differentiation strategy is prevalent when they criticize an opposition party. In contrast, the liberal party tends to concern with individualistic morality such as fairness. This result indicates that moral cleavage does exist between parties in South Korea. Furthermore, individualistic moral gaps of the two political parties are eased over time, which seems to be due to the discussion of economic democratization of conservative party that emerged after 2012, but the community-related moral gaps widened. These results imply that past political cleavages related to economic interests are diminishing and replaced by cultural and social values associated with communitarian morality. However, since the conservative party’s differentiation strategy is largely related to negative campaigns, it is doubtful whether such moral differentiation among political parties can contribute to the long-term party identification of the voters, thus further research is needed to determine it is sustainable. Despite the limitations, this study makes it possible to track and identify the moral changes of party system through automated text analysis. More generally, this study could contribute to the analysis of various texts associated with the moral foundation and finding a distributed representation of moral, ethical values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=moral%20foundations%20theory" title="moral foundations theory">moral foundations theory</a>, <a href="https://publications.waset.org/abstracts/search?q=moral%20politics" title=" moral politics"> moral politics</a>, <a href="https://publications.waset.org/abstracts/search?q=party%20system" title=" party system"> party system</a>, <a href="https://publications.waset.org/abstracts/search?q=Word2Vec" title=" Word2Vec "> Word2Vec </a> </p> <a href="https://publications.waset.org/abstracts/63431/the-evolution-of-moral-politics-analysis-on-moral-foundations-of-korean-parties" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63431.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">362</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">6</span> Stock Price Prediction with 'Earnings' Conference Call Sentiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sungzoon%20Cho">Sungzoon Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Hye%20Jin%20Lee"> Hye Jin Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungwhan%20Jeon"> Sungwhan Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongyoung%20Min"> Dongyoung Min</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungwon%20Lyu"> Sungwon Lyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=earnings%20call%20script" title="earnings call script">earnings call script</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20price%20prediction" title=" stock price prediction"> stock price prediction</a> </p> <a href="https://publications.waset.org/abstracts/86655/stock-price-prediction-with-earnings-conference-call-sentiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86655.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">5</span> TransDrift: Modeling Word-Embedding Drift Using Transformer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nishtha%20Madaan">Nishtha Madaan</a>, <a href="https://publications.waset.org/abstracts/search?q=Prateek%20Chaudhury"> Prateek Chaudhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Nishant%20Kumar"> Nishant Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Srikanta%20Bedathur"> Srikanta Bedathur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However, as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the flexibility of the transformer, our model accurately learns the dynamics of the embedding drift and predicts future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP%20applications" title="NLP applications">NLP applications</a>, <a href="https://publications.waset.org/abstracts/search?q=transformers" title=" transformers"> transformers</a>, <a href="https://publications.waset.org/abstracts/search?q=Word2vec" title=" Word2vec"> Word2vec</a>, <a href="https://publications.waset.org/abstracts/search?q=drift" title=" drift"> drift</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20embeddings" title=" word embeddings"> word embeddings</a> </p> <a href="https://publications.waset.org/abstracts/165423/transdrift-modeling-word-embedding-drift-using-transformer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165423.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">90</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">4</span> Incorporating Lexical-Semantic Knowledge into Convolutional Neural Network Framework for Pediatric Disease Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaocong%20Liu">Xiaocong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Huazhen%20Wang"> Huazhen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ting%20He"> Ting He</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaozheng%20Li"> Xiaozheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihan%20Zhang"> Weihan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Chen"> Jian Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20medical%20record" title=" electronic medical record"> electronic medical record</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20representation" title=" feature representation"> feature representation</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20semantics" title=" lexical semantics"> lexical semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20decision" title=" semantic decision"> semantic decision</a> </p> <a href="https://publications.waset.org/abstracts/137499/incorporating-lexical-semantic-knowledge-into-convolutional-neural-network-framework-for-pediatric-disease-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137499.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">125</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">3</span> A Review of Research on Pre-training Technology for Natural Language Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moquan%20Gong">Moquan Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-training" title=" pre-training"> pre-training</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20model" title=" language model"> language model</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20vectors" title=" word vectors"> word vectors</a> </p> <a href="https://publications.waset.org/abstracts/183121/a-review-of-research-on-pre-training-technology-for-natural-language-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183121.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">57</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Feature Engineering Based Detection of Buffer Overflow Vulnerability in Source Code Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst%20Shapna%20Akter">Mst Shapna Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossain%20Shahriar"> Hossain Shahriar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cyber%20security" title="cyber security">cyber security</a>, <a href="https://publications.waset.org/abstracts/search?q=vulnerability%20detection" title=" vulnerability detection"> vulnerability detection</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/160180/feature-engineering-based-detection-of-buffer-overflow-vulnerability-in-source-code-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160180.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">89</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">1</span> Application of Vector Representation for Revealing the Richness of Meaning of Facial Expressions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carmel%20Sofer">Carmel Sofer</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Vilenchik"> Dan Vilenchik</a>, <a href="https://publications.waset.org/abstracts/search?q=Ron%20Dotsch"> Ron Dotsch</a>, <a href="https://publications.waset.org/abstracts/search?q=Galia%20Avidan"> Galia Avidan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Glove" title="Glove">Glove</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20perception" title=" face perception"> face perception</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20expression%20perception." title=" facial expression perception. "> facial expression perception. </a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20expression%20production" title=" facial expression production"> facial expression production</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=word%20embedding" title=" word embedding"> word embedding</a>, <a href="https://publications.waset.org/abstracts/search?q=word2vec" title=" word2vec"> word2vec</a> </p> <a href="https://publications.waset.org/abstracts/83508/application-of-vector-representation-for-revealing-the-richness-of-meaning-of-facial-expressions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83508.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>