A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences

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 r...

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Bibliographic Details
Published in:TheScientificWorld 2014-01, Vol.2014 (2014), p.1-17
Main Authors: Lee, Ming Che, Hsieh, Tung Cheng, Chang, Jia Wei
Format: Article
Language:English
Subjects:
Algorithms
Computational linguistics
Grammar
Information retrieval
Language
Language processing
Methods
Natural language interfaces
Natural Language Processing
Semantics
Syntax
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Summary: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 ontology-based 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.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2014/437162