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Authors

Rie Kubota Ando

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Rie Kubota Ando

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Publications by Rie Kubota Ando (bibliography)

» 2005 «

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Ando, Rie Kubota and Zhang, Tong (2005): A High-Performance Semi-Supervised Learning Method for Text Chunking. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL) 2005, Ann Arbor, Michigan. . Available online

“In machine learning, whether one can build a more accurate classifier by using unlabeled data (semi-supervised learning) is an important issue. Although a number of semi-supervised methods have been proposed, their effectiveness on NLP tasks is not always clear. This paper presents a novel semi-supervised method that employs a learning paradigm which we call structural learning. The idea is to find ``what good classifiers are like'' by learning from thousands of automatically generated auxiliary classification problems on unlabeled data. By doing so, the common predictive structure shared by the multiple classification problems can be discovered, which can then be used to improve performance on the target problem. The method produces performance higher than the previous best results on CoNLL'00 syntactic chunking and CoNLL'03 named entity chunking (English and German).”

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Ando, Rie Kubota and Zhang, Tong (2005): A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. In Journal of Machine Learning Research, 6 pp. 1817-1853

“One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods are proposed, at the current stage, we still don't have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semi-supervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semi-supervised learning setting.”

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Ando, Rie Kubota, Dredze, Mark and Zhang, Tong (2005): TREC 2005 Genomics Track Experiments at IBM Watson. In: Proceedings of the Fourteenth Text REtrieval Conference (TREC 2005) November, 2005. . Available online

“This paper describes out experiments in the TREC 2005 Genomics Track. For the ad-hoc retrieval task, we study synonym-based query expansion, as well as the effectiveness of a new pseudo-relevance feedback method which is derived from our recent work on semi-supervised learning. For the categorization task, we study various methods for estimating conditional class probability and determining the optimal threshold parameter -- essential for obtaining high performance result for this task. ”

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» 2001 «

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Ando, Rie Kubota and Lee, Lillian (2001): Iterative residual rescaling. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2001. pp. 154-162. Available online

“We consider the problem of creating document representations in which inter-document similarity measurements correspond to semantic similarity. We first present a novel subspace-based framework for formalizing this task. Using this framework, we derive a new analysis of Latent Semantic Indexing(LSI), showing a precise relationship between its performance and the uniformityof the underlying distribution of documents over topics. This analysis helps explain the improvements gained by Ando's (2000) Iterative Residual Rescaling (IRR) algorithm: IRR can compensate for distributional non-uniformity. A further benefit of our framework is that it provides a well-motivated, effective method for automatically determining the rescaling factor IRR depends on, leading to further improvements. A series of experiments over various settings and with several evaluation metrics validates our claims.”

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» 2000 «

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Ando, Rie Kubota (2000): Latent Semantic Space: Iterative Scaling Improves Precision of Inter-Document Similarity Measurement. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2000. pp. 216-223. Available online

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Changes to this page (author)

27 Feb 2010: Enabled abstracts to be shown on Rie Kubota Ando's author page.
16 Sep 2007: Conference Article was added to the page (approved by an editor)

16 Sep 2007: Author was added to the bibliography (approved by an editor)
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Publication statistics

Publication period:2000-2005
Publication count:5
Number of co-authors:3

Productive colleagues

Rie Kubota Ando's 3 most productive colleagues in number of publications:

Tong Zhang:9
Mark Dredze:7
Lillian Lee:7

Collaboration count

Number of publications with 3 favourite co-authors:

Tong Zhang:3
Mark Dredze:1
Lillian Lee:1

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Learn more about Rie Kubota Ando:
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Conference Calendar

Mar 21

WritersUA Conf. for Software User Assistance

Mar 22

UX People

Mar 22

ETRA '10 - Eye Tracking Research & Applications Symp.

Mar 19

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