A Semantic Approach to Recognizing Textual Entailment

Author

Tatu, Marta and Moldovan, Dan

Conference

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

Year

2005

Figures & Tables

Table 5: The impact of the semantic axioms on each NLP application data set. T and F stand for True and False entailments, respectively.
Figure 1: T Semantics and H Semantics . The solid ar-
Table 7: The accuracy( ACC ) and f-measure( F ) performance values of our system
Table 3: Examples of semantic combination axioms
Table 4: Frames-related semantic rules
Table 1: Entailment proof example. Table 2 lists the semantic relations and their abbreviations. Sections 3.2 and 4.1 will detail the semantics behind the axioms T Axiom , T 1 Axiom 2 , T Axiom 3 , T Axiom 4 , and H Axiom . 1
Table 6: Applicability on the RTE data
Table 2: The set of semantic relations

Table of Contents

  • Abstract
  • 1 Recognizing Textual Entailment
  • 2 Approach
  • 3 Semantic Calculus
    • 3.1 Semantic relations
    • 3.2 Combinations of two semantic relations
  • 4 FrameNet Can Help
    • 4.1 Frame-based semantic axioms
    • 4.2 Context importance
  • 5 Experimental Results
    • 5.1 The RTE data
    • 5.2 Semantic axiom applicability
    • 5.3 RTE performance
  • 6 Conclusion
  • References

References

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