Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation

Yan Song, Fei Xia


Abstract
Domain adaptation is an important topic for natural language processing. There has been extensive research on the topic and various methods have been explored, including training data selection, model combination, semi-supervised learning. In this study, we propose to use a goodness measure, namely, description length gain (DLG), for domain adaptation for Chinese word segmentation. We demonstrate that DLG can help domain adaptation in two ways: as additional features for supervised segmenters to improve system performance, and also as a similarity measure for selecting training data to better match a test set. We evaluated our systems on the Chinese Penn Treebank version 7.0, which has 1.2 million words from five different genres, and the Chinese Word Segmentation Bakeoff-3 data.
Anthology ID:
L12-1580
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3853–3860
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/973_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Yan Song and Fei Xia. 2012. Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3853–3860, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Using a Goodness Measurement for Domain Adaptation: A Case Study on Chinese Word Segmentation (Song & Xia, LREC 2012)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/973_Paper.pdf