Gradable adjective embedding for commonsense knowledge

Kyungjae Lee, Hyunsouk Cho, Seung won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adjective understanding is crucial for answering qualitative or subjective questions, such as “is New York a big city”, yet not as sufficiently studied as answering factoid questions. Our goal is to project adjectives in the continuous distributional space, which enables to answer not only the qualitative question example above, but also comparative ones, such as “is New York bigger than San Francisco?”. As a basis, we build on the probability P(New York—big city) and P(Boston—big city) observed in Hearst patterns from a large Web corpus (as captured in a probabilistic knowledge base such as Probase). From this base model, we observe that this probability well predicts the graded score of adjective, but only for “head entities” with sufficient observations. However, the observation of a city is scattered to many adjectives – Cities are described with 194 adjectives in Probase, and, on average, only 2% of cities are sufficiently observed in adjective-modified concepts. Our goal is to train a distributional model such that any entity can be associated to any adjective by its distance from the vector of ‘big city’ concept. To overcome sparsity, we learn highly synonymous adjectives, such as big and huge cities, to improve prediction accuracy. We validate our finding with real-word knowledge bases.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsLongbing Cao, Kyuseok Shim, Jae-Gil Lee, Jinho Kim, Yang-Sae Moon, Xuemin Lin
PublisherSpringer Verlag
Pages814-827
Number of pages14
ISBN (Print)9783319575285
DOIs
Publication statusPublished - 2017 Jan 1
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 2017 May 232017 May 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10235 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
CountryKorea, Republic of
CityJeju
Period17/5/2317/5/26

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, K., Cho, H., & Hwang, S. W. (2017). Gradable adjective embedding for commonsense knowledge. In L. Cao, K. Shim, J-G. Lee, J. Kim, Y-S. Moon, & X. Lin (Eds.), Advances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings (pp. 814-827). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10235 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-57529-2_63