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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

verfasst von : Jiaming Shen, Zeqiu Wu, Dongming Lei, Jingbo Shang, Xiang Ren, Jiawei Han

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer International Publishing

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Abstract

Corpus-based set expansion (i.e., finding the “complete” set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search.
To discover new entities in an expanded set, previous approaches either make one-time entity ranking based on distributional similarity, or resort to iterative pattern-based bootstrapping. The core challenge for these methods is how to deal with noisy context features derived from free-text corpora, which may lead to entity intrusion and semantic drifting. In this study, we propose a novel framework, SetExpan, which tackles this problem, with two techniques: (1) a context feature selection method that selects clean context features for calculating entity-entity distributional similarity, and (2) a ranking-based unsupervised ensemble method for expanding entity set based on denoised context features. Experiments on three datasets show that SetExpan is robust and outperforms previous state-of-the-art methods in terms of mean average precision.
Code related to this chapter is available at: https://​github.​com/​mickeystroller/​SetExpan
Data related to this chapter are available at: https://​goo.​gl/​1suS3Z

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Vorheriges Kapitel Rethinking Unsupervised Feature Selection: From Pseudo Labels to Pseudo Must-Links
Nächstes Kapitel Bayesian Nonlinear Support Vector Machines for Big Data
Fußnoten
3
Results of SEISA on PubMed-CVD are omitted due to the scalability issue.
 
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Metadaten
Titel
SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble
verfasst von
Jiaming Shen
Zeqiu Wu
Dongming Lei
Jingbo Shang
Xiang Ren
Jiawei Han
Copyright-Jahr
2017
Buch
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71248-2

Electronic ISBN: 978-3-319-71249-9

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71249-9_18