Word association norms, mutual information, and lexicography
Computational Linguistics
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Textual entailment recognition based on dependency analysis and wordnet
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
The role and resolution of textual entailment in natural language processing applications
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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The variability of semantic expression is a special characteristic of natural language. This variability is challenging for many natural language processing applications that try to infer the same meaning from different text variants. In order to treat this problem a generic task has been proposed: Textual Entailment Recognition. In this paper, we present a new Textual Entailment approach based on Latent Semantic Indexing (LSI) and the cosine measure. This proposed approach extracts semantic knowledge from different corpora and resources. Our main purpose is to study how the acquired information can be combined with an already developed and tested Machine Learning Entailment system (MLEnt). The experiments show that the combination of MLEnt, LSI and cosine measure improves the results of the initial approach.