scholarly journals The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity

2014 ◽  
Author(s):  
Johannes Bjerva ◽  
Johan Bos ◽  
Rob van der Goot ◽  
Malvina Nissim
Keyword(s):  
Semantic Similarity ◽  
Formal Semantics ◽  
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.

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