NDLI: Chinese textual entailment with Wordnet semantic and dependency syntactic analysis

Content Provider	IEEE Xplore Digital Library
Author	Chun Tu Min-Yuh Day
Copyright Year	2013
Description	Author affiliation: Dept. of Inf. Manage., Tamkang Univ., Taipei, Taiwan (Chun Tu; Min-Yuh Day)
Abstract	Recognizing Inference in TExt (RITE) is a task for automatically detecting entailment, paraphrase, and contradiction in texts which addressing major text understanding in information access research areas. In this paper, we proposed a Chinese textual entailment system using Wordnet semantic and dependency syntactic approaches in Recognizing Inference in Text (RITE) using the NTCIR-10 RITE-2 subtask datasets. Wordnet is used to recognize entailment at lexical level. Dependency syntactic approach is a tree edit distance algorithm applied on the dependency trees of both the text and the hypothesis. We thoroughly evaluate our approach using NTCIR-10 RITE-2 subtask datasets. As a result, our system achieved 73.28% on Traditional Chinese Binary-Class (BC) subtask and 74.57% on Simplified Chinese Binary-Class subtask with NTCIR-10 RITE-2 development datasets. Thorough experiments with the text fragments provided by the NTCIR-10 RITE-2 subtask showed that the proposed approach can improve system's overall accuracy.
Starting Page	69
Ending Page	74
File Size	163534
Page Count	6
File Format	PDF
ISBN	9781479910502
DOI	10.1109/IRI.2013.6642455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Syntactics Accuracy Text recognition Training Support vector machines Predictive models Support Vector Machine (SVM) Textual Entailment Semantic Features Dependency Analysis WordNet Syntactic Features Machine Learning
Content Type	Text
Resource Type	Article

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