1. Transition-Based Dependency Parsing using Perceptron Learner
- Author
-
Iyer, Rahul Radhakrishnan, Ballesteros, Miguel, Dyer, Chris, and Frederking, Robert
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - 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., Comment: This was part of an assignment at my graduate course at LTI. This does not offer any major novelties
- Published
- 2020