Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text

Material type: 文書・図像類

Author: Pham, Minh Quang Nhatほか

Publisher: 北陸先端科学技術大学院大学情報科学研究科

Publication date: 2013-01-18

Material Format: Digital

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Note (General)：: The problem of text representation is an important issue in textual inference tasks. Given the fact that full predicate-logic analysis is not practica...

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Material Type: 文書・図像類
Title: Using Shallow Semantic Parsing and Relation Extraction for Finding Contradiction in Text
Author/Editor: Pham, Minh Quang Nhat
Nguyen, Minh Le
Shimazu, Akira
Author Heading: Pham, Minh Quang Nhat
Nguyen, Minh Le
Shimazu, Akira
Publication, Distribution, etc.: 北陸先端科学技術大学院大学情報科学研究科
Publication Date: 2013-01-18
Publication Date (W3CDTF): 2013-01-18
Periodical title: Research report (School of Information Science, Japan Advanced Institute of Science and Technology)
No. or year of volume/issue: IS-RR-2013-002
Volume: IS-RR-2013-002
Pages: 1-10
ISSN (Periodical Title): ISSN : 0918-7553
Text Language Code: eng
Target Audience: 一般
Note (General): The problem of text representation is an important issue in textual inference tasks. Given the fact that full predicate-logic analysis is not practical in wide-coverage semantic processing, using shallow semantic representations is an intuitive and straightforward approach. Previous work on finding contradiction in text incorporate information derived from predicate-argument structures as features in supervised machine learning frameworks. In contrast to previous work, we explore the use of shallow semantic representations for contradiction detection in a rule-based framework. We address the low-coverage problem of shallow semantic representations by using a backup module which relies on binary relations extracted from sentences for contradiction detection. Evaluation experiments conducted on standard data sets indicated that using the backup module increases the coverage of contradiction phenomena for the contradiction detection system. Our system achieves better recall and F1 score for contradiction detection than most of baseline methods, and the same recall as a state of the art supervised method for the task.
リサーチレポート（北陸先端科学技術大学院大学情報科学研究科）
identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/10901
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 北陸先端科学技術大学院大学 : JAIST学術研究成果リポジトリ