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Titlebook: Investigations in Entity Relationship Extraction; Sachin Sharad Pawar,Pushpak Bhattacharyya,Girish K Book 2023 The Editor(s) (if applicabl

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發(fā)表于 2025-3-21 19:55:18 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Investigations in Entity Relationship Extraction
編輯Sachin Sharad Pawar,Pushpak Bhattacharyya,Girish K
視頻videohttp://file.papertrans.cn/475/474799/474799.mp4
概述Highlights challenges and recent techniques developed for the extraction of complex relations.Covers a few domain-specific applications for joint extraction as well as complex relation extraction.Incl
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Investigations in Entity Relationship Extraction;  Sachin Sharad Pawar,Pushpak Bhattacharyya,Girish K Book 2023 The Editor(s) (if applicabl
描述The book covers several entity and relation extraction techniques starting from the traditional feature-based techniques to the recent techniques using deep neural models. Two important focus areas of the book are – i) joint extraction techniques where the tasks of entity and relation extraction are jointly solved, and ii) extraction of complex relations where relation types can be N-ary and cross-sentence. The first part of the book introduces the entity and relation extraction tasks and explains the motivation in detail. It covers all the background machine learning concepts necessary to understand the entity and relation extraction techniques explained later. The second part of the book provides a detailed survey of the traditional entity and relation extraction problems covering several techniques proposed in the last two decades. The third part of the book focuses on joint extraction techniques which attempt to address both the tasks of entity and relation extraction jointly. Several joint extraction techniques are surveyed and summarized in the book. It also covers two joint extraction techniques in detail which are based on the authors’ work. The fourth and the last part of
出版日期Book 2023
關(guān)鍵詞Natural Language Processing; Entity Relationship Extraction; Artificial Intelligence; Complex Relation
版次1
doihttps://doi.org/10.1007/978-981-19-5391-0
isbn_softcover978-981-19-5393-4
isbn_ebook978-981-19-5391-0Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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沙發(fā)
發(fā)表于 2025-3-21 23:49:15 | 只看該作者
Book 2023g deep neural models. Two important focus areas of the book are – i) joint extraction techniques where the tasks of entity and relation extraction are jointly solved, and ii) extraction of complex relations where relation types can be N-ary and cross-sentence. The first part of the book introduces t
板凳
發(fā)表于 2025-3-22 01:58:57 | 只看該作者
地板
發(fā)表于 2025-3-22 05:06:46 | 只看該作者
Introduction,With the advent of the Internet, a large amount of digital text is generated every day, such as news articles, research publications, blogs, social media, and question answering forums.
5#
發(fā)表于 2025-3-22 12:48:13 | 只看該作者
Literature Survey,In this chapter, we describe some of the relevant past literature on Relation Extraction.
6#
發(fā)表于 2025-3-22 14:32:02 | 只看該作者
Joint Inference for End-to-end Relation Extraction,As discussed in the previous chapter, better performance for end-to-end relation extraction is achieved when the extraction of entities and relations is carried out jointly.
7#
發(fā)表于 2025-3-22 17:19:16 | 只看該作者
8#
發(fā)表于 2025-3-22 22:56:06 | 只看該作者
Recent Advances in Entity and Relation Extraction,In this chapter, we describe a few recent advances in joint entity and relation extraction as well as N-ary cross-sentence relation extraction.
9#
發(fā)表于 2025-3-23 03:56:48 | 只看該作者
Conclusions,This monograph investigated two crucial problems in relation extraction: (i) end-to-end relation extraction involving joint extraction of entities and relations, and (ii) N-ary cross-sentence relation extraction.
10#
發(fā)表于 2025-3-23 07:35:47 | 只看該作者
https://doi.org/10.1007/978-981-19-5391-0Natural Language Processing; Entity Relationship Extraction; Artificial Intelligence; Complex Relation
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