/CCGSupertagging

LSTM CCG Supertagging

Primary LanguageCMIT LicenseMIT

CCGSupertagging

Implement a BiLSTM CCG Supertagging model.

Motivation:

Add a language model to the LSTM Tagger.

Model

CCG with LM

BiLSTM Model + LM on tags (Vaswani et al., 2016)

Result

Model Dev Accuracy Test Accuracy Test F1(CCGBank) Test F1(Wikipedia) Test F1(Bioinfer)
C&C(auto pos) 91.50 92.02 85.54 80.83 76.91
NN 91.10 91.57 86.13 82.00 79.19
RNN 93.07 93.00 87.07 82.49 79.14
LSTM 94.10 94.30 87.20 N.A. 80.50
LSTM+tri-training 94.90 94.70 88.10 N.A. 82.20
LSTM+LM 94.24 - 88.32 - -

References

  1. Xu, W., Auli, M., & Clark, S. (2015). CCG supertagging with a recurrent neural network. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Vol. 2, pp. 250-255).

  2. Vaswani, A., Bisk, Y., Sagae, K., & Musa, R. (2016). Supertagging with lstms. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 232-237).

  3. Lewis, M., Lee, K., & Zettlemoyer, L. (2016). Lstm ccg parsing. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 221-231).

  4. Lewis, M., & Steedman, M. (2014). Improved CCG parsing with semi-supervised supertagging. Transactions of the Association for Computational Linguistics, 2, 327-338.