Modeling semantic compositionality of relational patterns. (April 2016)

• Record Type: Journal Article
• Authors: Takase, Sho; Okazaki, Naoaki; Inui, Kentaro
• Is Part Of: Engineering applications of artificial intelligence. Volume 50(2016:Feb.)
• Page Start: 256
• Page End: 264
• Publication Date: 2016-04
• Subjects: Knowledge acquisition -- Natural language processing -- Relation extraction -- Recursive neural network -- Word embedding -- Semantic compositionality; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.01.027
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Vector representation is a common approach for expressing the meaning of a relational pattern. Most previous work obtained a vector of a relational pattern based on the distribution of its context words (e.g., arguments of the relational pattern), regarding the pattern as a single 'word'. However, this approach suffers from the data sparseness problem, because relational patterns are productive, i.e., produced by combinations of words. To address this problem, we propose a novel method for computing the meaning of a relational pattern based on the semantic compositionality of constituent words. We extend the Skip-gram model (Mikolov et al., 2013 ) to handle semantic compositions of relational patterns using recursive neural networks. The experimental results show the superiority of the proposed method for modeling the meanings of relational patterns, and demonstrate the contribution of this work to the task of relation extraction.