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Inventi Impact - Algorithm

Articles

  • Inventi:eal/85/14
    A GRAMMAR-BASED SEMANTIC SIMILARITY ALGORITHM FOR NATURAL LANGUAGE SENTENCES
    Ming Che Lee, Jia Wei Chang, Tung Cheng Hsieh

    This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontologybased approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

    How to Cite this Article
    CC Compliant Citation: Ming Che Lee, Jia Wei Chang, and Tung Cheng Hsieh, “A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences,” The Scientific World Journal, vol. 2014, Article ID 437162, 17 pages, 2014. doi:10.1155/2014/437162. Copyright © 2014 Ming Che Lee et al. This is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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