research-article

Integrating Multiple Dependency Corpora for Inducing Wide-Coverage Japanese CCG Resources

Authors:

Sumire Uematsu,

Takuya Matsuzaki,

Hiroki Hanaoka,

Hideki MimaAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 14, Issue 1

Article No.: 1, Pages 1 - 24

https://doi.org/10.1145/2658997

Published: 30 January 2015 Publication History

Abstract

A novel method to induce wide-coverage Combinatory Categorial Grammar (CCG) resources for Japanese is proposed in this article. For some languages including English, the availability of large annotated corpora and the development of data-based induction of lexicalized grammar have enabled deep parsing, i.e., parsing based on lexicalized grammars. However, deep parsing for Japanese has not been widely studied. This is mainly because most Japanese syntactic resources are represented in chunk-based dependency structures, while previous methods for inducing grammars are dependent on tree corpora. To translate syntactic information presented in chunk-based dependencies to phrase structures as accurately as possible, integration of annotation from multiple dependency-based corpora is proposed. Our method first integrates dependency structures and predicate-argument information and converts them into phrase structure trees. The trees are then transformed into CCG derivations in a similar way to previously proposed methods. The quality of the conversion is empirically evaluated in terms of the coverage of the obtained CCG lexicon and the accuracy of the parsing with the grammar. While the transforming process used in this study is specialized for Japanese, the framework of our method would be applicable to other languages for which dependency-based analysis has been regarded as more appropriate than phrase structure-based analysis due to morphosyntactic features.

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Cited By

Onishi MOgata SOkano KBekki D(2022)Reducing Syntactic Complexity for Information Extraction from Japanese Requirement Specifications2022 29th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC57359.2022.00051(387-396)Online publication date: Dec-2022
https://doi.org/10.1109/APSEC57359.2022.00051
Kashihara K(2020)Translate Japanese into Formal Languages with an Enhanced Generalization AlgorithmIntelligent Computing10.1007/978-3-030-52246-9_47(638-655)Online publication date: 4-Jul-2020
https://doi.org/10.1007/978-3-030-52246-9_47
Ambati BDeoskar TSteedman M(2018)Hindi CCGbankLanguage Resources and Evaluation10.1007/s10579-017-9379-652:1(67-100)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1007/s10579-017-9379-6

Index Terms

Integrating Multiple Dependency Corpora for Inducing Wide-Coverage Japanese CCG Resources
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 14, Issue 1

January 2015

83 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/2730923

Editor:
Richard Sproat
Google, Inc., USA

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 January 2015

Accepted: 01 July 2014

Revised: 01 March 2014

Received: 01 December 2013

Published in TALLIP Volume 14, Issue 1

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Cited By

Onishi MOgata SOkano KBekki D(2022)Reducing Syntactic Complexity for Information Extraction from Japanese Requirement Specifications2022 29th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC57359.2022.00051(387-396)Online publication date: Dec-2022
https://doi.org/10.1109/APSEC57359.2022.00051
Kashihara K(2020)Translate Japanese into Formal Languages with an Enhanced Generalization AlgorithmIntelligent Computing10.1007/978-3-030-52246-9_47(638-655)Online publication date: 4-Jul-2020
https://doi.org/10.1007/978-3-030-52246-9_47
Ambati BDeoskar TSteedman M(2018)Hindi CCGbankLanguage Resources and Evaluation10.1007/s10579-017-9379-652:1(67-100)Online publication date: 17-Dec-2018
https://dl.acm.org/doi/10.1007/s10579-017-9379-6

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