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.
%0 Journal Article
%1 lee2014grammarbased
%A Lee, Ming Che
%A Chang, Jia Wei
%A Hsieh, Tung Cheng
%D 2014
%J The Scientific World Journal
%K benchmarking matching nlp semantic semantic-measure semantic-measures
%P 17
%T A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences
%U https://www.hindawi.com/journals/tswj/2014/437162/
%V 2014
%X 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.
@article{lee2014grammarbased,
abstract = {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.},
added-at = {2018-08-28T10:14:14.000+0200},
author = {Lee, Ming Che and Chang, Jia Wei and Hsieh, Tung Cheng},
biburl = {https://www.bibsonomy.org/bibtex/21726bb1140819cb07497318adc3199d8/karime},
interhash = {f7c756a4d274edd924f88ef8b4ba34a6},
intrahash = {1726bb1140819cb07497318adc3199d8},
journal = {The Scientific World Journal},
keywords = {benchmarking matching nlp semantic semantic-measure semantic-measures},
pages = 17,
timestamp = {2018-08-28T10:14:14.000+0200},
title = {A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences},
url = {https://www.hindawi.com/journals/tswj/2014/437162/},
volume = 2014,
year = 2014
}