Predicting simplified thematic progression pattern for discourse analysis

Xuefeng Xi, Victor S. Sheng, Shuhui Yang, Baochuan Fu, Zhiming Cui

Research output: Contribution to journalArticlepeer-review

Abstract

The pattern of thematic progression, reflecting the semantic relationships between contextual two sentences, is an important subject in discourse analysis. We introduce a new corpus of Chinese news discourses annotated with thematic progression information and explore some computational methods to automatically extracting the discourse structural features of simplified thematic progression pattern (STPP) between contextual sentences in a text. Furthermore, these features are used in a hybrid approach to a major discourse analysis task, Chinese coreference resolution. This novel approach is built up via heuristic sieves and a machine learning method that comprehensively utilizes both the top-down STPP features and the bottom-up semantic features. Experimental results on the intersection of the CoNLL-2012 task shared dataset and the CDTC corpus demonstrate the effectiveness of our proposed approach.

Original languageEnglish
Pages (from-to)163-181
Number of pages19
JournalComputers, Materials and Continua
Volume63
Issue number1
DOIs
StatePublished - Mar 3 2020

Keywords

  • Discourse topic
  • Thematic progression
  • Theme-rheme theory

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