Transition-Based Dependency Parsing using Perceptron Learner

• URL: http://arxiv.org/abs/2001.08279v2
• Date: Tue, 28 Jan 2020 22:09:19 GMT
• Title: Transition-Based Dependency Parsing using Perceptron Learner
• Authors: Rahul Radhakrishnan Iyer, Miguel Ballesteros, Chris Dyer, Robert Frederking
• Abstract summary: 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.
• Score: 34.59241394911966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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