Combining Part-of-Speech Tags and Self-Attention Mechanism for Simile Recognition

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Pages (from-to)163864-163876
Journal / PublicationIEEE Access
Volume7
Online published5 Nov 2019
Publication statusPublished - 2019

Abstract

Simile recognition is to find simile sentences and extract the tenor and vehicle from these sentences. Previous works illustrate that tenors and vehicles are typically noun phrases. A word may have different part-of-speech (POS) labels (e.g., adjectives, adverbs, nouns, and verbs) in different sentences. It is important for the simile recognition task to identify a certain POS information for each word in a sentence. However, existing models use the same word embedding to represent a word, which cannot accurately represent the POS information of this word in different sentences. In this paper, we propose a neural network framework explicitly integrating the POS information into simile recognition task, with additional self-attention mechanism to better capture long term dependencies between any two tokens in sentences. The experimental results show that our proposed models significantly outperform previous state-of-the-art methods in the simile recognition task. We also present an analysis showing that the POS information and self-attention mechanism are effective for the simile recognition task.

Research Area(s)

  • Simile recognition, part-of-speech, self-attention mechanism

Citation Format(s)

Combining Part-of-Speech Tags and Self-Attention Mechanism for Simile Recognition. / Zhang, Pengfei; Cai, Yi; Chen, Junying; Chen, Wenhao; Song, Hengjie.

In: IEEE Access, Vol. 7, 2019, p. 163864-163876.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal