NLP with Neural Networks

This lecture introduces the task of dependency parsing and presents the two popular kinds of dependency parsers: transition-based parsers and graph-based parsers.

Lectures

• Introduction to dependency parsing

• Transition-based parsing

• Graph-based parsing

• Evaluating dependency parsers

Readings

• The chapter on dependency parsing in Jurafsky and Martin’s textbook.

• Sandra Kübler, Ryan McDonald, and Joakim Nivre. 2009. Dependency Parsing. Synthesis Lectures on Human Language Technologies. Springer International Publishing.

• Danqi Chen and Christopher D Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. In EMNLP.

• Timothy Dozat and Christopher D. Manning. 2016. Deep Biaffine Attention for Neural Dependency Parsing. In International Conference on Learning Representations.

• Eliyahu Kiperwasser and Yoav Goldberg. 2016. Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations. Transactions of the Association for Computational Linguistics, 4:313–327.