Corpus ID: 12141896

When logical inference helps determining textual entailment ( and when it doesn ’ t )

@inproceedings{BosWhenLI,
  title={When logical inference helps determining textual entailment ( and when it doesn ’ t )},
  author={Johan Bos and K. Markert}
}
We compare and combine two methods to approach the second textual entailment challenge (RTE-2): a shallow method based mainly on word-overlap and a method based on logical inference, using first-order theorem proving and model building techniques. We use a machine learning technique to combine features of both methods. We submitted two runs, one using only the shallow features, yielding an accuracy of 61.6%, and one using features of both methods, performing with an accuracy score of 60.6… CONTINUE READING
76 Citations

Tables from this paper

Semantic and Logical Inference Model for Textual Entailment
  • 28
  • PDF
Relation Alignment for Textual Entailment Recognition
  • 41
  • PDF
Natural Logic for Textual Inference
  • 159
  • Highly Influenced
  • PDF
Recognizing Textual Entailment with Deep-Shallow Semantic Analysis and Logical Inference
  • 4
Recognizing textual entailment : Rational , evaluation and approaches
  • I D O D A G A N, O. A.N, B E R N A R D O M A G N I N I
  • 2009
  • 144
  • PDF
Combining Theorem Proving with Natural Language Processing
  • 8
  • PDF
Automatic Theorem Proving for Natural Logic: a Case Study on Textual Entailment
  • PDF
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
  • 3
  • PDF
Learning Alignments and Leveraging Natural Logic
  • 90
  • PDF

References

SHOWING 1-10 OF 10 REFERENCES
Recognising Textual Entailment with Logical Inference
  • 243
  • PDF
Towards Wide-Coverage Semantic Interpretation
  • 100
  • PDF
The PASCAL Recognising Textual Entailment Challenge
  • 1,434
  • PDF
Representation and Inference for Natural Language: A First Course in Computational Semantics
  • 284
  • PDF
The design and implementation of VAMPIRE
  • 512
Patrick Blackburn and Johan Bos, Representation and Inference for Natural Language
  • 114
Data Mining Practical Machine Learning Tools And Techniques With Java Implementations
  • 394
  • Highly Influential
Automatic Proofs and Counterexamples for Some Ortholattice Identities
  • W. McCune
  • Computer Science
  • Inf. Process. Lett.
  • 1998
  • 38
From Discourse to Logic; An Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and DRT
  • Kluwer, Dordrecht.
  • 1993