Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction

  title={Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction},
  author={Yichun Yin and Furu Wei and Li Dong and Kaimeng Xu and Ming Zhang and Ming Zhou},
In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r ⇡ w2 in the low-dimensional space, where the multihop dependency paths are treated as a sequence of grammatical relations and modeled by a recurrent neural network… CONTINUE READING
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