Skip to main content
  • 353 Accesses

Abstract

In this chapter, we summarize the entire book. In particular, we list the approaches introduced in this book in a table. We then discuss the approaches further.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sites.google.com/site/sancl2012/

References

  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends®; in Machine Learning, 2(1), 1–127.

    Article  Google Scholar 

  • Chen, D., & Manning, C. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha (pp. 740–750). Association for Computational Linguistics. http://www.aclweb.org/anthology/D14-1082.

  • Chen, W., Kawahara, D., Uchimoto, K., Zhang, Y., & Isahara, H. (2008). Dependency parsing with short dependency relations in unlabeled data. In Proceedings of IJCNLP 2008, Hyderabad.

    Google Scholar 

  • Chen, W., Kazama, J., Uchimoto, K., & Torisawa, K. (2009). Improving dependency parsing with subtrees from auto-parsed data. In Proceedings of EMNLP 2009, Singapore (pp. 570–579).

    Google Scholar 

  • Chen, W., Zhang, M., & Li, H. (2012). Utilizing dependency language models for graph-based dependency parsing models. In Proceedings of ACL 2012, Jeju.

    Google Scholar 

  • Chen, W., Zhang, M., & Zhang, Y. (2013). Semi-supervised feature transformation for dependency parsing. In Proceedings of EMNLP 2013, Seattle (pp. 1303–1313). Association for Computational Linguistics. http://www.aclweb.org/anthology/D13-1129.

  • Chen, W., Zhang, Y., & Zhang, M. (2014). Feature embeddings for dependency parsing. In Proceedings of coling 2014, Dublin.

    Google Scholar 

  • Hatori, J., Matsuzaki, T., Miyao, Y., & Tsujii, J. (2011). Incremental joint POS tagging and dependency parsing in Chinese. In Proceedings of 5th international joint conference on natural language processing, Chiang Mai (pp. 1216–1224). Asian Federation of Natural Language Processing. http://www.aclweb.org/anthology/I11-1136.

  • Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.

    Article  Google Scholar 

  • Kong, L., Schneider, N., Swayamdipta, S., Bhatia, A., Dyer, C., & Smith, N. A. (2014). A dependency parser for tweets. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha (pp. 1001–1012). Association for Computational Linguistics. http://www.aclweb.org/anthology/D14-1108.

  • Koo, T., Carreras, X., & Collins, M. (2008). Simple semi-supervised dependency parsing. In Proceedings of ACL-08: HLT, Columbus.

    Google Scholar 

  • Le, P., & Zuidema, W. (2014). The inside-outside recursive neural network model for dependency parsing. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha (pp. 729–739). Association for Computational Linguistics. http://www.aclweb.org/anthology/D14-1081.

  • Li, Z., Zhang, M., Che, W., & Liu, T. (2012). A separately passive-aggressive training algorithm for joint POS tagging and dependency parsing. In Proceedings of the 24rd international conference on computational linguistics (Coling 2012), Mumbai. Coling 2012 Organizing Committee.

    Google Scholar 

  • Li, Z., Zhang, M., & Chen, W. (2014). Ambiguity-aware ensemble training for semi-supervised dependency parsing. In Proceedings of annual meeting of the association for computational linguistics (ACL2014), Baltimore (pp. 457–467, 22–27).

    Google Scholar 

  • McDonald, R., Petrov, S., & Hall, K. (2011). Multi-source transfer of delexicalized dependency parsers. In Proceedings of the conference on empirical methods in natural language processing, Edinburgh (pp. 62–72). Association for Computational Linguistics.

    Google Scholar 

  • Sagae, K., & Tsujii, J. (2007). Dependency parsing and domain adaptation with LR models and parser ensembles. In Proceedings of the CoNLL shared task session of EMNLP-CoNLL 2007, Prague (pp. 1044–1050).

    Google Scholar 

  • Søgaard, A., & Rishøj, C. (2010). Semi-supervised dependency parsing using generalized tri-training. In Proceedings of ACL, Uppsala (pp. 1065–1073).

    Google Scholar 

  • Spreyer, K., & Kuhn, J. (2009). Data-driven dependency parsing of new languages using incomplete and noisy training data. In CoNLL, Boulder (pp. 12–20)

    Google Scholar 

  • Suzuki, J., Isozaki, H., Carreras, X., & Collins, M. (2009). An empirical study of semi-supervised structured conditional models for dependency parsing. In Proceedings of EMNLP2009, Singapore (pp. 551–560). Association for Computational Linguistics.

    Google Scholar 

  • Suzuki, J., Isozaki, H., & Nagata, M. (2011). Learning condensed feature representations from large unsupervised data sets for supervised learning. In Proceedings of ACL2011, Portland (pp. 636–641). Association for Computational Linguistics. http://www.aclweb.org/anthology/P11-2112.

  • Täckström, O., McDonald, R., & Nivre, J. (2013). Target language adaptation of discriminative transfer parsers. In Proceedings of NAACL, Atlanta (pp. 1061–1071).

    Google Scholar 

  • Täckström, O., McDonald, R., & Uszkoreit, J. (2012). Cross-lingual word clusters for direct transfer of linguistic structure. In Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies, Montréal (pp. 477–487). Association for Computational Linguistics.

    Google Scholar 

  • van Noord, G. (2007). Using self-trained bilexical preferences to improve disambiguation accuracy. In Proceedings of IWPT-07, Prague.

    Google Scholar 

  • Wang, W. Y., Kong, L., Mazaitis, K., & Cohen, W. W. (2014). Dependency parsing for weibo: An efficient probabilistic logic programming approach. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), Doha (pp. 1152–1158). Association for Computational Linguistics. http://www.aclweb.org/anthology/D14-1122.

  • Zhang, M., Zhang, Y., Che, W., & Liu, T. (2014). Character-level chinese dependency parsing. In Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 1: long papers), Baltimore (pp. 1326–1336). Association for Computational Linguistics. http://www.aclweb.org/anthology/P14-1125.

  • Zhou, G., Zhao, J., Liu, K., & Cai, L. (2011). Exploiting web-derived selectional preference to improve statistical dependency parsing. In Proceedings of ACL-HLT2011, Portland (pp. 1556–1565). Association for Computational Linguistics. http://www.aclweb.org/anthology/P11-1156.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Chen, W., Zhang, M. (2015). Closing Remarks. In: Semi-Supervised Dependency Parsing. Springer, Singapore. https://doi.org/10.1007/978-981-287-552-5_10

Download citation

Publish with us

Policies and ethics