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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/3906

Title: Co-Training of Conditional Random Fields for Segmenting Sequence Data
Authors: Xuan-Hieu, Phan
Le-Minh, Nguyen
Inoguchi, Yasushi
Keywords: semi-supervised learning
co-training
conditional random fields
text labeling and segmentation
Issue Date: Nov-2005
Publisher: JAIST Press
Abstract: This paper presents a semi-supervised co-training approach for discriminative sequential learning models, such as conditional random fields (CRFs). In this framework, different CRF models are trained on an initial set of sequence data according different views. The bootstrapping process is performed by iteratively adding new reliably inferred data sequences to the training data sets of CRF models retraining them. Reliable data sequences are inferred from a huge set of unlabeled data by estimating entropy values of predicted labels at time positions in data sequences. The inference and re-train operations are repeated a number of times in order that each CRF model should gain as much useful evidence from unlabeled data and the other CRF models as possible. The proposed method was tested on noun phrase chunking and achieved significant results.
Description: The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html
IFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2116, Kobe, Japan
Symposium 5, Session 2 : Data/Text Mining from Large Databases Text Mining
Language: ENG
URI: http://hdl.handle.net/10119/3906
ISBN: 4-903092-02-X
Appears in Collections:IFSR 2005

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