Two-pass Discourse Segmentation with Pairing and Global Features
Published in
arXiv.org(2014)External Links:
TL
TL;DR
It is shown that both the pairing and global features are useful on their own, and their combination achieved an F1 of 92.6% of identifying insentence discourse boundaries, which is a 17.8% error-rate reduction over the state of the art performance.
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 F1 of 92.6% of identifying insentence discourse boundaries, which is a 17.8% error-rate reduction over the stateof-the-art performance, approaching 95% of human performance. In addition, similar improvement is observed across different classification frameworks.
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