Automatic Adaptation of Annotation Standards: Chinese Word Segmentation and POS Tagging – A Case Study

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 522–530, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Automatic Adaptation of Annotation Standards: Chinese Word Segmentation and POS Tagging – A Case Study Wenbin Jiang † Liang Huang ‡ Qun Liu † †Key Lab. of Intelligent Information Processing ‡Google Research Institute of Computing Technology 1350 Charleston Rd. Chinese Academy of Sciences Mountain View, CA 94043, USA P.O. Box 2704, Beijing 100190, China lianghuang@google.com {jiangwenbin, liuqun}@ict.ac.cn liang.huang.sh@gmail.com Abstract Manually annotated corpora are valuable but scarce resources, yet for many anno- tation tasks such as treebanking and se- quence labeling there exist multiple cor- pora with different and incompatible anno- tation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice to automatically adapt one annotation standard to another. We present a simple yet effective strategy that transfers knowledge from a differently an- notated corpus to the corpus with desired annotation. We test the efficacy of this method in the context of Chinese word segmentation and part-of-speech tagging, where no segmentation and POS tagging standards are widely accepted due to the lack of morphology in Chinese. Experi- ments show that adaptation from the much larger People’s Daily corpus to the smaller but more popular Penn Chinese Treebank results in significant improvements in both segmentation and tagging accuracies (with error reductions of 30.2% and 14%, re- spectively), which in turn helps improve Chinese parsing accuracy. 1 Introduction Much of statistical NLP research relies on some sort of manually annotated corpora to train their models, but these resources are extremely expen- sive to build, especially at a large scale, for ex- ample in treebanking (Marcus et al., 1993). How- ever the linguistic theories underlying these anno- tation efforts are often heavily debated, and as a re- sult there often exist multiple corpora for the same task with vastly different and incompatible anno- tation philosophies. For example just for English treebanking there have been the Chomskian-style �1 �2 �3 �4 �5 �6 NR NN VV NR U.S. Vice-President visited China �1 �2 �3 �4 �5 �6 ns b n v U.S. Vice President visited-China Figure 1: Incompatible word segmentation and POS tagging standards between CTB (upper) and People’s Daily (below). Penn Treebank (Marcus et al., 1993) the HPSG LinGo Redwoods Treebank (Oepen et al., 2002), and a smaller dependency treebank (Buchholz and Marsi, 2006). A second, related problem is that the raw texts are also drawn from different do- mains, which for the above example range from financial news (PTB/WSJ) to transcribed dialog (LinGo). These two problems seem be a great waste in human efforts, and it would be nice if one could automatically adapt from one annota- tion standard and/or domain to another in order to exploit much larger datasets for better train- ing. The second problem, domain adaptation, is very well-studied, e.g. by Blitzer et al. (2006) and Daum´e III (2007) (and see below for discus- …

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