Article

Learning syntactic patterns for automatic hypernym discovery

Authors:

Daniel Jurafsky,

Andrew Y. NgAuthors Info & Claims

NIPS'04: Proceedings of the 17th International Conference on Neural Information Processing Systems

Pages 1297 - 1304

Published: 01 December 2004 Publication History

Abstract

Semantic taxonomies such as WordNet provide a rich source of knowledge for natural language processing applications, but are expensive to build, maintain, and extend. Motivated by the problem of automatically constructing and extending such taxonomies, in this paper we present a new algorithm for automatically learning hypernym (is-a) relations from text. Our method generalizes earlier work that had relied on using small numbers of hand-crafted regular expression patterns to identify hypernym pairs. Using "dependency path" features extracted from parse trees, we introduce a general-purpose formalization and generalization of these patterns. Given a training set of text containing known hypernym pairs, our algorithm automatically extracts useful dependency paths and applies them to new corpora to identify novel pairs. On our evaluation task (determining whether two nouns in a news article participate in a hypernym relationship), our automatically extracted database of hypernyms attains both higher precision and higher recall than WordNet.

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Chou CChang CLin YChien K(2020)On the Construction of Web NER Model Training Tool based on Distant SupervisionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/342281719:6(1-28)Online publication date: 15-Nov-2020
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Huang JXie YMeng YZhang YHan JGupta RLiu YShah MRajan STang JPrakash B(2020)CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation TransferringProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403244(1928-1936)Online publication date: 23-Aug-2020
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Learning syntactic patterns for automatic hypernym discovery
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image Guide Proceedings

NIPS'04: Proceedings of the 17th International Conference on Neural Information Processing Systems

December 2004

1656 pages

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 December 2004

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Cited By

Chou CChang CLin YChien K(2020)On the Construction of Web NER Model Training Tool based on Distant SupervisionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/342281719:6(1-28)Online publication date: 15-Nov-2020
https://dl.acm.org/doi/10.1145/3422817
Huang JXie YMeng YZhang YHan JGupta RLiu YShah MRajan STang JPrakash B(2020)CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation TransferringProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403244(1928-1936)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403244
Rai SChakraverty S(2020)A Survey on Computational Metaphor ProcessingACM Computing Surveys10.1145/337326553:2(1-37)Online publication date: 20-Mar-2020
https://dl.acm.org/doi/10.1145/3373265
Chu CRazniewski SWeikum G(2019)TiFi: Taxonomy Induction for Fictional DomainsThe World Wide Web Conference10.1145/3308558.3313519(2673-2679)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313519
Wang CFan YHe XZhou A(2019)A Family of Fuzzy Orthogonal Projection Models for Monolingual and Cross-lingual Hypernymy PredictionThe World Wide Web Conference10.1145/3308558.3313439(1965-1976)Online publication date: 13-May-2019
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Sarkhel RNandi ABoncz PManegold SAilamaki ADeshpande AKraska T(2019)Visual Segmentation for Information Extraction from Heterogeneous Visually Rich DocumentsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319867(247-262)Online publication date: 25-Jun-2019
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Zha HChen WLi KYan XTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Mining Algorithm Roadmap in Scientific PublicationsProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330913(1083-1092)Online publication date: 25-Jul-2019
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Qin CZhu HZhu CXu TZhuang FMa CZhang JXiong HTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)DuerQuizProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330706(2165-2173)Online publication date: 25-Jul-2019
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