Assessing sentence similarity through lexical, syntactic and semantic analysis. (September 2016)

• Record Type: Journal Article
• Authors: Ferreira, Rafael; Lins, Rafael Dueire; Simske, Steven J.; Freitas, Fred; Riss, Marcelo
• Is Part Of: Computer speech & language. Volume 39(2016)
• Page Start: 1
• Page End: 28
• Publication Date: 2016-09
• Subjects: Graph-based model -- Sentence simplification -- Relation extraction -- Inductive logic programming; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2016.01.003
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: A new sentence similarity measure based on lexical, syntactic, semantic analysis. It combines statistical and semantic methods to measure similarity between words. The measure was evaluated using state-of-art datasets: Li et al., SemEval 2012, CNN. It presents an application to eliminate redundancy in multi-document summarization. Abstract: The degree of similarity between sentences is assessed by sentence similarity methods. Sentence similarity methods play an important role in areas such as summarization, search, and categorization of texts, machine translation, etc. The current methods for assessing sentence similarity are based only on the similarity between the words in the sentences. Such methods either represent sentences as bag of words vectors or are restricted to the syntactic information of the sentences. Two important problems in language understanding are not addressed by such strategies: the word order and the meaning of the sentence as a whole. The new sentence similarity assessment measure presented here largely improves and refines a recently published method that takes into account the lexical, syntactic and semantic components of sentences. The new method was benchmarked using Li–McLean, showing that it outperforms the state of the art systems and achieves results comparable to the evaluation made by humans. Besides that, the method proposed was extensively tested using the SemEval 2012 sentence similarity test set and in the evaluation of the degree of similarity between summaries using the CNN-corpus. In both cases, the measure proposed here was proved effective and useful.