Automatic Recognition of Focus and Interrogative Word in Chinese Question for Classification


  •  Zhichang Zhang    
  •  Yu Zhang    
  •  Ting Liu    
  •  Sheng Li    

Abstract

Question classification is one of the most important components in a question answering (QA) system. When there are fewer features in a question can be used for classification, the interrogative word and focus in question are critical features. Most previous studies in question classification used heuristic rules to identify the focus and interrogative word in question. In this paper, a statistical method is explored to automatically label them for Chinese question using condition random fields (CRFs) model. The features for CRFs model are extracted from word segmentation, part-of-speech (POS) tagging, named entity recognition, and dependency parsing results. A knowledge base HowNet is also used. The experimental results show that the precision for interrogative word recognition is 98.97% and 90.85% of focus can be correctly recognized in a free available Chinese question data set.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-8989
  • Issn(Onlne): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 18.20

h-index (January 2018): 23

i10-index (January 2018): 90

h5-index (January 2018): 11

h5-median(January 2018):17

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