Definite Description Resolution Enrichment with WordNet Domain Labels
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Discovery of inference rules for question-answering
Natural Language Engineering
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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The need of the current Natural Language Processing applications to identify text segments that express the same meaning in different ways, evolved into the identification of semantic variability expressions. Most of the developed approaches focus on the text structure, such as the word overlaps, the distance between phrases or syntactic trees, word to word similarity, logic representation among others. However, current research did not identify how the global conceptual representation of a sentences can contribute to the resolution of this problem. In this paper, we present an approach where the meaning of a sentence is represented with the associated relevant domains. In order to determine the semantic relatedness among text segments, Latent Semantic Analysis is used. We demonstrate, evaluate and analyze the contribution of our conceptual representation approach in an evaluation with the paraphrase task.