Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction

@inproceedings{Yin2016UnsupervisedWA,
  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},
  booktitle={IJCAI},
  year={2016}
}
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
Highly Cited
This paper has 24 citations. REVIEW CITATIONS
18 Citations
38 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 18 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 38 references

In NIPS

  • Tomas Mikolov, Ilya Sutskever, +4 authors their compositionality
  • pages 3111–3119,
  • 2013
Highly Influential
11 Excerpts

Cl

  • Guang Qiu, Bing Liu, Jiajun Bu, Chun Chen. Opinion word expansion, target extraction through double propagation
  • 37(1):9–27,
  • 2011
Highly Influential
3 Excerpts

In Conference on Artificial Intelligence

  • Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio. Learning structured embeddings of knowledg bases
  • number EPFL-CONF-192344,
  • 2011
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…