title: A grammar-based semantic similarity algorithm for natural language sentences.
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.
authors: Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan.
authors: Ming Che
authors: 0000-0002-4400-9109
authors: MC
authors: Lee
authors: Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan.
authors: Jia Wei
authors: JW
authors: Chang
authors: Department of Visual Communication Design, Hsuan Chuang University, Hsinchu 300, Taiwan.
authors: Tung Cheng
authors: TC
authors: Hsieh
mesh_terms: D000465:Algorithms; D009323:Natural Language Processing; D012660:Semantics