A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences
Joint Authors
Lee, Ming Che
Chang, Jia Wei
Hsieh, Tung Cheng
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-10
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Lee, Ming Che& Chang, Jia Wei& Hsieh, Tung Cheng. 2014. A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-17.
https://search.emarefa.net/detail/BIM-1049632
Modern Language Association (MLA)
Lee, Ming Che…[et al.]. A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences. The Scientific World Journal No. 2014 (2014), pp.1-17.
https://search.emarefa.net/detail/BIM-1049632
American Medical Association (AMA)
Lee, Ming Che& Chang, Jia Wei& Hsieh, Tung Cheng. A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-17.
https://search.emarefa.net/detail/BIM-1049632
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049632