Transition-Based Dependency Parsing using Perceptron Learner
Computation and Language
2020-01-30 v2 Artificial Intelligence
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.
Keywords
Cite
@article{arxiv.2001.08279,
title = {Transition-Based Dependency Parsing using Perceptron Learner},
author = {Rahul Radhakrishnan Iyer and Miguel Ballesteros and Chris Dyer and Robert Frederking},
journal= {arXiv preprint arXiv:2001.08279},
year = {2020}
}
Comments
This was part of an assignment at my graduate course at LTI. This does not offer any major novelties