scholarly journals Towards a Semantic Model for Textual Entailment Annotation

2014 ◽  
Vol 9 ◽  
Author(s):  
Assaf Toledo ◽  
Stavroula Alexandropoulou ◽  
Sophie Chesney ◽  
Sophia Katrenko ◽  
Heidi Klockmann ◽  
...  
Keyword(s):  
Formal Semantic ◽  
Manual Annotation ◽  
Semantic Model ◽  
Annotation Scheme ◽  
Proposed Model ◽  
Textual Entailment ◽  
Recognizing Textual Entailment ◽  
Semantic Operators

We introduce a new formal semantic model for annotating textual entailments that describes restrictive, intersective, and appositive modification. The model contains a formally defined interpreted lexicon, which specifies the inventory of symbols and the supported semantic operators, and an informally defined annotation scheme that instructs annotators in which way to bind words and constructions from a given pair of premise and hypothesis to the interpreted lexicon. We explore the applicability of the proposed model to the Recognizing Textual Entailment (RTE) 1–4 corpora and describe a first-stage annotation scheme on which we based the manual annotation work. The constructions we annotated were found to occur in 80.65% of the entailments in RTE 1–4 and were annotated with cross-annotator agreement of 68% on average. The annotated parts of the RTE corpora are publicly available for further research.

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.

scholarly journals Approximate Analyzing of Labeled Transition Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Qiong Yu ◽  
Shihan Yang ◽  
Jinzhao Wu
Keyword(s):  
Control Systems ◽  
Linear Systems ◽  
Matrix Decomposition ◽  
Transition System ◽  
Formal Semantic ◽  
Semantic Model ◽  
Transition Systems ◽  
State Space Explosion ◽  
Normal Circumstance ◽  
Time Linear

As the most important formal semantic model, labeled transition systems are widely used, which can describe the general concurrent systems or control systems without disturbance. However, under normal circumstance, transition systems are complex and difficult to use due to large amount of calculation and the state space explosion problems. In order to overcome these problems, approximate equivalent labeled transition systems are proposed by means of incomplete low-up matrix decomposition factorization. This technique can reduce the complexity of computation and calculate under the allowing errors. As for continuous-time linear systems, we develop a modeling method of approximated transition system based on the approximate solution of matrix, which provides a facility for approximately formal semantic modeling for linear systems and to effectively analyze errors. An example of application in the context of linear systems without disturbances is studied.

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