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

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.