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2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Semi-supervised Learning Framework for Cross-Lingual Projection
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
Cross-lingual projection encounters two major challenges, the noise from word-alignment error and the syntactic divergences between two languages. To solve these two problems, a semi-supervised learning framework of cross-lingual projection is proposed to get better annotations using parallel data. Moreover, a projection model is introduced to model the projection process of labeling from the resource-rich language to the resource-scarce language. The projection model, together with the traditional target model of cross-lingual projection, can be seen as two views of parallel data. Utilizing these two views, an extension of co-training algorithm to structured predictions is designed to boost the result of the two models. Experiments show that the proposed cross-lingual projection method improves the accuracy in the task of POS-tagging projection. And using only one-to-one alignments proves to lead to more accurate results than using all kinds of alignment information.
Index Terms:
cross-lingual projection, semi-supervised learning, structured predictions, pos tagging, co-training
Citation:
PengLong Hu, Mo Yu, Jing Li, CongHui Zhu, TieJun Zhao, "Semi-supervised Learning Framework for Cross-Lingual Projection," wi-iat, vol. 3, pp.213-216, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
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