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Experiments on pattern-based relation learning

Authors:

Willy Yap,

Timothy BaldwinAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1657 - 1660

https://doi.org/10.1145/1645953.1646197

Published: 02 November 2009 Publication History

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Abstract

Relation extraction is the task of extracting semantic relations - such as synonymy or hypernymy - between word pairs from corpus data. Past work in relation extraction has concentrated on manually creating templates to use in directly extracting word pairs for a given semantic relation from corpus text. Recently, there has been a move towards using machine learning to automatically learn these patterns. We build on this research by running experiments investigating the impact of corpus type, corpus size and different parameter settings on learning a range of lexical relations.

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Schulte im Walde SFrassinelli D(2022)Distributional Measures of Semantic AbstractionFrontiers in Artificial Intelligence10.3389/frai.2021.7967564Online publication date: 8-Feb-2022
https://doi.org/10.3389/frai.2021.796756
Liang LTian F(2020)Using normal dictionaries to extract multiple semantic relationshipsThe Journal of Engineering10.1049/joe.2019.1212Online publication date: 13-Jan-2020
https://doi.org/10.1049/joe.2019.1212
Zhao JWang JYang JJin P(2019)Company Acquisition Relations Extraction From Web PagesSemantic Web Science and Real-World Applications10.4018/978-1-5225-7186-5.ch001(1-17)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7186-5.ch001
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Index Terms

Experiments on pattern-based relation learning
1. Computing methodologies
  1. Machine learning
    1. Learning settings

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CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
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Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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Schulte im Walde SFrassinelli D(2022)Distributional Measures of Semantic AbstractionFrontiers in Artificial Intelligence10.3389/frai.2021.7967564Online publication date: 8-Feb-2022
https://doi.org/10.3389/frai.2021.796756
Liang LTian F(2020)Using normal dictionaries to extract multiple semantic relationshipsThe Journal of Engineering10.1049/joe.2019.1212Online publication date: 13-Jan-2020
https://doi.org/10.1049/joe.2019.1212
Zhao JWang JYang JJin P(2019)Company Acquisition Relations Extraction From Web PagesSemantic Web Science and Real-World Applications10.4018/978-1-5225-7186-5.ch001(1-17)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7186-5.ch001
Kamel MTrojahn CGhamnia AAussenac-Gilles NFabre C(2017)A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation PagesProcedia Computer Science10.1016/j.procs.2017.08.208112:C(1764-1773)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.08.208

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Chinese Relation Extraction with Bi-directional Context-Based Lattice LSTM

Towards Large-Scale Unsupervised Relation Extraction from the Web

A Machine learning Filter for Relation Extraction

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Chinese Relation Extraction with Bi-directional Context-Based Lattice LSTM

Towards Large-Scale Unsupervised Relation Extraction from the Web

A Machine learning Filter for Relation Extraction

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