Paper: Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging

ACL ID E14-1062
Title Type-Supervised Domain Adaptation for Joint Segmentation and POS-Tagging
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We report an empirical investigation on type-supervised domain adaptation for joint Chinese word segmentation and POS-tagging, making use of domain- specific tag dictionaries and only un- labeled target domain data to improve target-domain accuracies, given a set of annotated source domain sentences. Pre- vious work on POS-tagging of other lan- guages showed that type-supervision can be a competitive alternative to token- supervision, while semi-supervised tech- niques such as label propagation are important to the effectiveness of type- supervision. We report similar findings using a novel approach for joint Chinese segmentation and POS-tagging, under a cross-domain setting. With the help of un- labeled sentences and a lexicon of 3,000 words, we obtain 33% error reduction in target-domain...