@inproceedings{9d55a63fb17443eeb094f7d27cdda5de,
title = "Fine-grained named entity recognition using conditional random fields for question answering",
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 = "Conditional random fields, Fine-grained named entity recognition, Question answering",
author = "Changki Lee and Hwang, \{Yi Gyu\} and Oh, \{Hyo Jung\} and Soojong Lim and Jeong Heo and Lee, \{Chung Hee\} and Kim, \{Hyeon Jin\} and Wang, \{Ji Hyun\} and Jang, \{Myung Gil\}",
year = "2006",
doi = "10.1007/11880592\_49",
language = "English",
isbn = "3540457801",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "581--587",
booktitle = "Information Retrieval Technology - Third Asia Information Retrieval Symposium, AIRS 2006, Proceedings",
note = "3rd Asia Information Retrieval Symposium, AIRS 2006 ; Conference date: 16-10-2006 Through 18-10-2006",
}