Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment

2010 ◽  
pp. 366-375 ◽  
Author(s):  
Julio J. Castillo ◽  
Marina E. Cardenas
Keyword(s):  
Semantic Similarity ◽  
Textual Entailment ◽  
Recognizing Textual Entailment

A Lexico-Syntactic-Semantic Approach to Recognizing Textual Entailment

2020 ◽  
pp. 187-227 ◽  
Author(s):  
Rohini Basak ◽  
Sudip Kumar Naskar ◽  
Alexander Gelbukh
Keyword(s):  
Detailed Analysis ◽  
Semantic Similarity ◽  
Dependency Graph ◽  
Experimental Results ◽  
Classification Task ◽  
Semantic Approach ◽  
Textual Entailment ◽  
Lexical Similarity ◽  
Matching Rules ◽  
Recognizing Textual Entailment

Given two textual fragments, called a text and a hypothesis, respectively, recognizing textual entailment (RTE) is a task of automatically deciding whether the meaning of the second fragment (hypothesis) logically follows from the meaning of the first fragment (text). The chapter presents a method for RTE based on lexical similarity, dependency relations, and semantic similarity. In this method, called LSS-RTE, each of the two fragments is converted to a dependency graph, and the two obtained graph structures are compared using dependency triple matching rules, which have been compiled after a thorough and detailed analysis of various RTE development datasets. Experimental results show 60.5%, 64.4%, 62.8%, and 61.5% accuracy on the well-known RTE1, RTE2, RTE3, and RTE4 datasets, respectively, for the two-way classification task and 54.3% accuracy for three-way classification task on the RTE4 dataset.

scholarly journals Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference

2019 ◽  
Vol 33 ◽  
pp. 7410-7417 ◽  
Author(s):  
Masashi Yoshikawa ◽  
Koji Mineshima ◽  
Hiroshi Noji ◽  
Daisuke Bekki
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Processing Speed ◽  
Processing Time ◽  
State Of The Art ◽  
Proof Automation ◽  
New Knowledge ◽  
Textual Entailment ◽  
Amount Of Knowledge ◽  
Recognizing Textual Entailment

In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.

Sign in / Sign up

Export Citation Format

Share Document