Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention

Abstract

Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.

10 pages; PACLIC 2018

References in corpus (3)

• Efficient Estimation of Word Representations in Vector Space
• A Convolutional Neural Network for Modelling Sentences
• Tree-to-Sequence Attentional Neural Machine Translation

Cited by in corpus (1)

• Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks