With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.

In this paper we address issues related to building a large-scale Chinese corpus. We try to answer four questions: (i) how to speed up annotation, (ii) how to maintain high annotation quality, (iii) for what purposes is the corpus applicable, and finally (iv) what future work we anticipate.

In this paper we report on our experiments on automatic Word Sense Disambiguation using a maximum entropy approach for both English and Chinese verbs. We compare the difficulty of the sensetagging tasks in the two languages and investigate the types of contextual features that are useful for each language. Our experimental results suggest that while richer linguistic features are useful for English WSD, they may not be as beneficial for Chinese.

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