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arXiv 2001.082792020-01-28cs.CLcs.AIcs.LG

Transition-Based Dependency Parsing using Perceptron Learner

Rahul Radhakrishnan Iyer, Miguel Ballesteros, Chris Dyer, Robert Frederking

备注：: This was part of an assignment at my graduate course at LTI. This does not offer any major novelties

英文摘要：

Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models that can be trained on syntactically annotated corpora. In this paper, we tackle transition-based dependency parsing using a Perceptron Learner. Our proposed model, which adds more relevant features to the Perceptron Learner, outperforms a baseline arc-standard parser. We beat the UAS of the MALT and LSTM parsers. We also give possible ways to address parsing of non-projective trees.

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