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Titlebook: Information Retrieval Technology; 12th Asia Informatio Shaoping Ma,Ji-Rong Wen,Xin Zhao Conference proceedings 2016 Springer International

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发表于 2025-3-21 16:30:36 | 显示全部楼层 |阅读模式
书目名称Information Retrieval Technology
副标题12th Asia Informatio
编辑Shaoping Ma,Ji-Rong Wen,Xin Zhao
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Information Retrieval Technology; 12th Asia Informatio Shaoping Ma,Ji-Rong Wen,Xin Zhao Conference proceedings 2016 Springer International
描述.This book constitutes the refereed proceedings of the 12th Information Retrieval Societies Conference, AIRS 2016, held in Beijing, China, in November/December 2016...The 21 full papers presented together with 11 short papers were carefully reviewed and selected from 57 submissions. The final programme of AIRS 2015 is divided in the following tracks: IR models and theories; machine learning and data mining for IR; IR applications and user modeling; personalization and recommendation; and IR evaluation..
出版日期Conference proceedings 2016
关键词information retrieval; optimization; recommender systems; social media; web mining; healthcare; network em
版次1
doihttps://doi.org/10.1007/978-3-319-48051-0
isbn_softcover978-3-319-48050-3
isbn_ebook978-3-319-48051-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2016
The information of publication is updating

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Detecting Synonymous Predicates from Online Encyclopedia with Rich Featuresure selection and their effectiveness analysis. We not only leverage different resources such as Wikipedia, Freebase, but also use different word embeddings to represent predicates. The experimental results indicate that wikitext defined by Wikipedia and predicate surface form are most useful features.
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Learning a Semantic Space of Web Search via Session Datassion data, where a search session is regarded as an instantiation of an information need and keeps the interaction information of queries and URLs. Vector learning is done on a large query log from a search engine, and the efficacy of learnt vectors is examined in a few tasks.
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TLINE: Scalable Transductive Network Embeddingormance in node classification task, we test our methods on two real world network datasets, which are Citeseer and DBLP. The experimental result indicates that TLINE outperforms state-of-the-art baselines and is suitable for large-scale networks.
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Constraining Word Embeddings by Prior Knowledge – Application to Medical Information Retrievale). In this paper, we leverage the existing knowledge (word relations) in the medical domain to constrain word embeddings using the principle that related words should have similar embeddings. The resulting constrained word embeddings are used to rerank documents, showing superior effectiveness to unsupervised word embeddings.
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