Open Access

ARTICLE

Predicting Simplified Thematic Progression Pattern for Discourse Analysis

Xuefeng Xi¹, Victor S. Sheng^{1, 2, *}, Shuhui Yang³, Baochuan Fu¹, Zhiming Cui¹

1 School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
2 Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA.
3 Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323, USA.

* Corresponding Author: Victor S. Sheng. Email: .

Computers, Materials & Continua 2020, 63(1), 163-181. https://doi.org/10.32604/cmc.2020.06992

Received 22 April 2019; Accepted 29 April 2019; Issue published 30 March 2020

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.

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Keywords

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