Two-pass Discourse Segmentation with Pairing and Global Features
Abstract
Previous attempts at RST-style discourse segmentation typically adopt features centered on a single token to predict whether to insert a boundary before that token. In contrast, we develop a discourse segmenter utilizing a set of pairing features, which are centered on a pair of adjacent tokens in the sentence, by equally taking into account the information from both tokens. Moreover, we propose a novel set of global features, which encode characteristics of the segmentation as a whole, once we have an initial segmentation. We show that both the pairing and global features are useful on their own, and their combination achieved an of 92.6% of identifying in-sentence discourse boundaries, which is a 17.8% error-rate reduction over the state-of-the-art performance, approaching 95% of human performance. In addition, similar improvement is observed across different classification frameworks.
Cite
@article{arxiv.1407.8215,
title = {Two-pass Discourse Segmentation with Pairing and Global Features},
author = {Vanessa Wei Feng and Graeme Hirst},
journal= {arXiv preprint arXiv:1407.8215},
year = {2014}
}