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Titlebook: Natural Language Processing and Information Systems; 23rd International C Max Silberztein,Faten Atigui,Farid Meziane Conference proceedings

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發(fā)表于 2025-3-21 19:13:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Natural Language Processing and Information Systems
副標(biāo)題23rd International C
編輯Max Silberztein,Faten Atigui,Farid Meziane
視頻videohttp://file.papertrans.cn/662/661825/661825.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Natural Language Processing and Information Systems; 23rd International C Max Silberztein,Faten Atigui,Farid Meziane Conference proceedings
描述.This book constitutes the refereed proceedings of the 23rd International?Conference on Applications of Natural Language to Information Systems,?NLDB 2018, held in Paris, France, in June 2018.?.The 18 full papers, 26 short papers, and 9 poster papers presented were?carefully reviewed and selected from 99 submissions. The papers are organized in?the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based Models and Applications; Neural Networks Based Approaches; Ontology Engineering; NLP; Text Similarities and Plagiarism Detection; Text Classification; Information Mining; Recommendation Systems; Translation and Foreign Language Querying; Software Requirement and Checking..
出版日期Conference proceedings 2018
關(guān)鍵詞artificial intelligence; classification; computational linguistics; language; data mining; information re
版次1
doihttps://doi.org/10.1007/978-3-319-91947-8
isbn_softcover978-3-319-91946-1
isbn_ebook978-3-319-91947-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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發(fā)表于 2025-3-21 21:10:48 | 只看該作者
Automatic Identification and Classification of Misogynistic Language on Twitterorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.
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發(fā)表于 2025-3-22 04:22:00 | 只看該作者
Assessing the Effectiveness of Affective Lexicons for Depression Classificationlop two lexical feature engineering strategies for incorporating those lexicons into a supervised classifier. The effectiveness of different lexicons and feature engineering strategies are evaluated on a depression dataset collected from LiveJournal.
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發(fā)表于 2025-3-22 06:09:18 | 只看該作者
Conference proceedings 20182018, held in Paris, France, in June 2018.?.The 18 full papers, 26 short papers, and 9 poster papers presented were?carefully reviewed and selected from 99 submissions. The papers are organized in?the following topical sections: Opinion Mining and Sentiment Analysis in Social Media; Semantics-Based
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A Proposal for Book Oriented Aspect Based Sentiment Analysis: Comparison over Domainsent analysis. It contains book reviews published in Amazon, which is a new domain of application in . literature. The annotation process and its characteristics are described, as well as a comparison with other datasets. This paper focuses on this comparison, addressing the different subtasks and analyzing their performance and properties.
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發(fā)表于 2025-3-22 22:22:45 | 只看該作者
Semantic Mapping of Security Events to Known Attack Patternsns in Common Attack Pattern Enumeration and Classification (CAPEC). The system has been deployed in our partner company and when evaluated against the recommendations of two security analysts, achieved an f-measure of 64.57%.
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發(fā)表于 2025-3-23 06:38:13 | 只看該作者
Arabic Question Classification Using Support Vector Machines and Convolutional Neural Networksstage, we identify the coarse/main question class using an SVM model; in the second stage, for each coarse question class returned by the SVM model, a CNN model is used to predict the subclass (finer class) of the main class. The performed tests have shown that our approach to Arabic Questions Classification yields very promising results.
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