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Densely-connected neural networks for aspect term extraction

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References

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61433015).

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Authors and Affiliations

  1. School of Software and Microelectronics, Peking University, Beijing, 100871, China

    Chen Chen & Qingqing Zhu

  2. MOE Key Lab of Computational Linguistics, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Houfeng Wang

  3. National Engineering Research Center for Software Engineering (Peking University), Beijing, 100871, China

    Junfei Liu

Authors
  1. Chen Chen
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  2. Houfeng Wang
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  3. Qingqing Zhu
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  4. Junfei Liu
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Corresponding authors

Correspondence to Chen Chen or Houfeng Wang.

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Conclusion

We present a densely-connected neural network for the aspect term extraction task. It enables preserving feature information from the bottommost layer to the uppermost layer in deep neural networks. The experiment results on two standard benchmark ABSA datasets indicate that our model improves ATE performances and leads to new advanced results.

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Cite this article

Chen, C., Wang, H., Zhu, Q. et al. Densely-connected neural networks for aspect term extraction. Sci. China Inf. Sci. 65, 169103 (2022). https://doi.org/10.1007/s11432-019-2775-9

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  • DOI: https://doi.org/10.1007/s11432-019-2775-9

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