End to End Chinese Coreference Resolution with Structural Information

• Published in: Jisuanji gongcheng
• Main Author: FU Jian, KONG Fang
• Format: Article
• Language: English
• Published: Editorial Office of Computer Engineering 2020-01-01
• Subjects: end to end coreference resolution|structural embedding|part of speech|constituency tree|document syntactic compression tree
• Online Access: https://www.ecice06.com/fileup/1000-3428/PDF/20200106.pdf
• ISSN: 1000-3428

On the basis of the end to end coreference resolution model proposed by LEE et al.,this paper further considers the characteristics of Chinese writing and proposes a Chinese coreference resolution model with structural information.The constituency tree of all sentences is compressed to obtain the leaf node depth of the document compression tree.The Structural Embedding of Constituency Tree(SECT) is used to vectorize the structural information.The part of speech,the leaf node depth and the SECT information are introduced into the model as three eigenvectors for Chinese coreference resolution.The test results on the CoNLL2012 dataset show that the application of the three eigenvectors can effectively improve the Chinese coreference resolution of the proposed model,whose average <i>F</i>₁ value can reach 62.33%,which is 5.28% higher than the baseline.