Paper Info
Title
Integrating statistical and lexical information for recognizing textual entailments in text
Abstract
Recognizing textual entailment is to infer that a given text span follows from the meaning of a given hypothesis. To have better recognition capability, it is necessary to employ deep text processing units such as syntactic parsers and semantic taggers. However, these resources are not usually available in other non-English languages. In this paper, we present a light-weight Chinese textual entailment recognition system using part-of-speech information only. We designed two different feature models from training data and employed the well-known kernel method to learn to predict testing data. One feature set abstracts the generic statistics between the text pairs, while the other set directly models lexical features based on the traditional bag-of-words model. The ability of the proposed feature models not only brings additional statistical information from their datasets but also helps to enhance the prediction capability. To validate this, we conducted the experiments on the novel benchmark corpus - NTCIR-RITE-2011. The empirical results demonstrate that our method achieves the best results in comparison to the other competitors. In terms of accuracy, our method achieves 54.77% for the NTCIR RITE MC task.
Year
DOI
Venue
2013
10.1016/j.knosys.2012.11.009
Knowl.-Based Syst.
Keywords
Field
DocType
better recognition capability,text span,proposed feature model,different feature model,additional statistical information,light-weight chinese textual entailment,text pair,well-known kernel method,lexical information,deep text processing unit,models lexical,kernel methods,natural language processing,machine learning,textual entailment,text mining
Text graph,Data mining,Textual entailment,Computer science,Natural language processing,Artificial intelligence,Syntax,Text processing,Text mining,Test data,Parsing,Kernel method,Machine learning
Journal
Volume
ISSN
Citations 
40,
0950-7051
2
PageRank 
References 
Authors
0.37
16
1
Name
Order
Citations
PageRank
Yu-Chieh Wu124723.16