Increasing the Quality and Quantity of Source Language Data for Unsupervised Cross-Lingual POS Tagging
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چکیده
Bilingual corpora offer a promising bridge between resource-rich and resource-poor languages, enabling the development of natural language processing systems for the latter. English is often selected as the resource-rich language, but another choice might give better performance. In this paper, we consider the task of unsupervised cross-lingual POS tagging, and construct a model that predicts the best source language for a given target language. In experiments on 9 languages, this model improves on using a single fixed source language. We then show that further improvements can be made by combining information from multiple source languages.
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تاریخ انتشار 2013