Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (4): 255-261.doi: 10.11896/jsjkx.240100155

• Artificial Intelligence • Previous Articles     Next Articles

Automatic Identification and Classification of Topical Discourse Markers

YANG Jincai1, YU Moyang1, HU Man1, XIAO Ming2   

  1. 1 School of Computer Science,Central China Normal University,Wuhan 430079,China
    2 Research Center for Language and Language Education,Central China Normal University,Wuhan 430079,China
  • Received:2024-01-22 Revised:2024-05-16 Online:2025-04-15 Published:2025-04-14
  • About author:YANG Jincai,born in 1976,professor,doctoral supervisor,is a member of CCF(No.35662M).His main research interests include advanced database and information system,Chinese information processing,artificial intelligence and natural language processing.
  • Supported by:
    National Social Science Fundation of China(19BYY092) and Humanity and Social Science Foundation of Ministry of Education of China(20YJA740047).

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Abstract: Discourse markers,a kind of linguistic markers at the pragmatic level which have functions of organizing discourse,guiding signifier,and expressing emotions,have attracted extensive attention in linguistics.The accurate identification of discourse markers and categories plays an important role in the comprehension of text and the grasp of the speaker’s intention and emotion.In the past decade,scholars at home and abroad have conducted research on function,characteristics,sources and systematic classification of discourse markers and achieved rich results.However,due to the changeable forms,diverse sources,abstract features,and variants,it is difficult for machines to automatically identify discourse markers.In this paper,an NFLAT pointer network model integrating external linguistic features is proposed,which takes topical discourse markers as the research object,and realizes the automatic recognition and classification of discourse markers in discourse.Experimental results show that the precision of the trained model for the recognition and classification of topical discourse markers reaches 94.55%.

Key words: Discourse marker, Semantic enhancement, Feature fusion, Automatic identification and classification

CLC Number: 

  • TP391
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