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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/4663

Title: High-Performance Training of Conditional Random Fields for Large-Scale Applications of Labeling Sequence Data
Authors: PHAN, Xuan-Hieu
Keywords: parallel computing
probabilistic graphical models
conditional random fields
structured prediction
text processing
Issue Date: 2007-01-01
Publisher: 電子情報通信学会
Magazine name: IEICE TRANSACTIONS on Information and Systems
Volume: E90-D
Number: 1
Start page: 13
End page: 21
DOI: 10.1093/ietisy/e90-d.1.13
Abstract: Conditional random fields (CRFs) have been successfully applied to various applications of predicting and labeling structured data, such as natural language tagging & parsing, image segmentation & object recognition, and protein secondary structure prediction. The key advantages of CRFs are the ability to encode a variety of overlapping, non-independent features from empirical data as well as the capability of reaching the global normalization and optimization. However, estimating parameters for CRFs is very time-consuming due to an intensive forward-backward computation needed to estimate the likelihood function and its gradient during training. This paper presents a high-performance training of CRFs on massively parallel processing systems that allows us to handle huge datasets with hundreds of thousand data sequences and millions of features. We performed the experiments on an important natural language processing task (text chunking) on large-scale corpora and achieved significant results in terms of both the reduction of computational time and the improvement of prediction accuracy.
Rights: Copyright (C)2007 IEICE. Xuan-Hieu Phan, Le-Minh Nguyen, Yasushi Inoguchi and Susumu Horiguchi, IEICE TRANSACTIONS on Information and Systems, E90-D(1), 2007, 13-21. http://www.ieice.org/jpn/trans_online/
URI: http://hdl.handle.net/10119/4663
Material Type: publisher
Appears in Collections:e10-1. 雑誌掲載論文 (Journal Articles)

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