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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/7867

Title: Improving discriminative sequential learning by discovering important association of statistics
Authors: Phan, Xuan-Hieu
Nguyen, Le-Minh
Inoguchi, Yasushi
Ho, Tu-Bao
Horiguchi, Susumu
Keywords: Discriminative sequential learning
feature selection
association rule mining
information extraction
text segmentation
Issue Date: 2006-12
Publisher: Association for Computing Machinery
Magazine name: ACM Transactions on Asian Language Information Processing
Volume: 5
Number: 4
Start page: 413
End page: 438
DOI: 10.1145/1236181.1236187
Abstract: Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing or information extraction. Their key advantage is the ability to capture various nonindependent and overlapping features of inputs. However, several unexpected pitfalls have a negative influence on the model's performance; these mainly come from a high imbalance among classes, irregular phenomena, and potential ambiguity in the training data. This article presents a data-driven approach that can deal with such difficult data instances by discovering and emphasizing important conjunctions or associations of statistics hidden in the training data. Discovered associations are then incorporated into these models to deal with difficult data instances. Experimental results of phrase-chunking and named entity recognition using CRFs show a positive improvement in accuracy. In addition to the technical perspective, our approach also highlights a potential connection between association mining and statistical learning by offering an alternative strategy to enhance learning performance with interesting and useful patterns discovered from large datasets.
Rights: (c) ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Asian Language Information Processing, 5(4), 2006, 413-438. http://doi.acm.org/10.1145/1236181.1236187
URI: http://hdl.handle.net/10119/7867
Material Type: author
Appears in Collections:e10-1. 雑誌掲載論文 (Journal Articles)

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