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Conference Paper
Fine-Grained Named Entity Recognition Using Conditional Random Fields for Question Answering
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- Authors
- Chang ki Lee, Yi-Gyu Hwang, Hyo-Jung Oh, Soo Jong Lim, Jeong Heo, Chung-Hee Lee, Hyeon-Jin Kim, Ji-Hyun Wang, Myung-Gil Jang
- Issue Date
- 2006-10
- Citation
- Asia Information Retrieval Symposium (AIRS) 2006 (LNCS 4182), v.4182, pp.581-587
- Language
- English
- Type
- Conference Paper
- 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. © Springer-Verlag Berlin Heidelberg 2006.
- KSP Keywords
- Baseline model, Conditional Random Field(CRF), Named Entity Recognition, Passage retrieval, QA system, Training time, coarse-grained, fine-grained, maximum entropy(ME), named entity recognizer, question answering