A Sequential Model for Discourse Segmentation

Identifying discourse relations in a text is essential for various tasks in Natural Language Processing, such as automatic text summarization, question-answering, and dialogue generation. The first step of this process is segmenting a text into elementary units. In this paper, we present a novel model of discourse segmentation based on sequential data labeling. Namely, we use Conditional Random Fields to train a discourse segmenter on the RST Discourse Treebank, using a set of lexical and syntactic features. Our system is compared to other statistical and rule-based segmenters, including one based on Support Vector Machines. Experimental results indicate that our sequential model outperforms current state-of-the-art discourse segmenters, with an F-score of 0.94. This performance level is close to the human agreement F-score of 0.98.

• Type: Conference
• Year: 2010
• Where: CICLING
• Authors: Hugo Hernault, Danushka Bollegala, Mitsuru Ishizuka
• Keywords: CICLING 2010, Discourse, Discourse Segmenters, Natural Language Processing, RST Discourse Treebank