Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
A Joint Learning Approach to Explicit Discourse Parsing via Structured Perceptron
verfasst von : Sheng Li, Fang Kong, Guodong Zhou
Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Discourse parsing is a challenging task and plays a critical role in discourse analysis. In this paper, we focus on building an end-to-end PDTB-style explicit discourse parser via structured perceptron by decomposing it into two components, i.e., a connective labeler, which identifies connectives from a text and determines their senses in classifying discourse relationship, and an argument labeler, which identifies corresponding arguments for a given connective. Particularly, to reduce error propagation and incorporate the interaction between the two components, a joint learning approach via structured perceptron is proposed. Evaluation on the PDTB corpus shows that our two-components explicit discourse parser can achieve comparable performance with the state-of-the-art one. It also shows that our joint learning approach can significantly outperform the pipeline ones.