Chapter

Information Retrieval Technology

Volume 4182 of the series Lecture Notes in Computer Science pp 581-587

Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering

  • Changki LeeAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Yi-Gyu HwangAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Hyo-Jung OhAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Soojong LimAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Jeong HeoAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Chung-Hee LeeAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Hyeon-Jin KimAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Ji-Hyun WangAffiliated withElectronics and Telecommunications Research Institute (ETRI)
  • , Myung-Gil JangAffiliated withElectronics and Telecommunications Research Institute (ETRI)

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Abstract

In many QA systems, fine-grained named entities are extracted by coarse-grained named entity recognizer and fine-grained named entity dictionary. In this paper, we describe a fine-grained Named Entity Recognition using Conditional Random Fields (CRFs) for question answering. We used CRFs to detect boundary of named entities and Maximum Entropy (ME) to classify named entity classes. Using the proposed approach, we could achieve an 83.2% precision, a 74.5% recall, and a 78.6% F1 for 147 fined-grained named entity types. Moreover, we reduced the training time to 27% without loss of performance compared to a baseline model. In the question answering, The QA system with passage retrieval and AIU archived about 26% improvement over QA with passage retrieval. The result demonstrated that our approach is effective for QA.

Keywords

Fine-Grained Named Entity Recognition Conditional Random Fields Question Answering