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Titlebook: Semantic Web Challenges; Third SemWebEval Cha Harald Sack,Stefan Dietze,Christoph Lange Conference proceedings 2016 Springer International

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发表于 2025-3-21 18:10:12 | 显示全部楼层 |阅读模式
书目名称Semantic Web Challenges
副标题Third SemWebEval Cha
编辑Harald Sack,Stefan Dietze,Christoph Lange
视频video
概述Aims at developing a set of common benchmasrks, established evaluation procedures, tasks and datasets in the field of semantic web..Contains detailed record of the one of the most important internatio
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Semantic Web Challenges; Third SemWebEval Cha Harald Sack,Stefan Dietze,Christoph Lange Conference proceedings 2016 Springer International
描述.This book constitutes the thoroughly refereed post conference proceedings of the third edition of the Semantic Web Evaluation Challenge, SemWebEval 2016, co-located with the 13th European Semantic Web conference, held in Heraklion, Crete, Greece, in May/June 2016..This book includes the descriptions of all methods and tools that competed at SemWebEval 2016, together with a detailed description of the tasks, evaluation procedures and datasets. The contributions are grouped in the areas: Open Knowledge Extraction (OKE 2016); Semantic Sentiment Analysis (SSA 2016); Question Answering over Linked Data (QALD 6); Top-K Shortest Path in Large Typed RDF Graphs Datasets; Semantic Publishing (SemPub2016)..
出版日期Conference proceedings 2016
关键词artificial intelligence; benchmarks; graph search; information retrieval; knowledge extraction; knowledge
版次1
doihttps://doi.org/10.1007/978-3-319-46565-4
isbn_softcover978-3-319-46564-7
isbn_ebook978-3-319-46565-4Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer International Publishing Switzerland 2016
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Challenge on Fine-Grained Sentiment Analysis Within ESWC2016e number of opinions, emotions, sentiments that are being expressed within social media grows at an exponential rate; all these data can be exploited in order to come up with useful insights, analytics, etc. Initial Sentiment Analysis systems used lexical and statistical resources to automatically a
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Sentiment Polarity Detection from Amazon Reviews: An Experimental Studyasp the goodness of products. Mining and understanding the polarity of reviews is therefore crucially important for future customers that seek opinions and sentiments to support their decision buying process. This paper proposes an experimental study of SentiME, our approach for extracting the senti
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