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

PACLIC 2018  ·  Ryosuke Miyazaki, Mamoru Komachi ·

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.

PDF Abstract PACLIC 2018 PDF PACLIC 2018 Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here