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Espresso: leveraging generic patterns for automatically harvesting semantic relations

Authors:

Patrick Pantel,

Marco PennacchiottiAuthors Info & Claims

ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

Pages 113 - 120

https://doi.org/10.3115/1220175.1220190

Published: 17 July 2006 Publication History

Abstract

In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the Web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. We present an empirical comparison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision.

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ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

July 2006

1214 pages

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Association for Computational Linguistics

United States

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Published: 17 July 2006

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https://dl.acm.org/doi/10.1145/3442442.3451387
Nguyen TRazniewski SWeikum G(2021)Advanced Semantics for Commonsense Knowledge ExtractionProceedings of the Web Conference 202110.1145/3442381.3449827(2636-2647)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449827
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Jebbara SBasile VCabrio ECimiano Pd’Amato CLawrynowicz ALehmann J(2019)Extracting common sense knowledge via triple ranking using supervised and unsupervised distributional modelsSemantic Web10.3233/SW-18030210:1(139-158)Online publication date: 1-Jan-2019
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