In this paper, we describe the Sinica-Yuntech system (TeamID: SinicaNLP) at the NTCIR-10 MedNLP task. Materials of the MedNLP task are in Japanese. However, having only Chinese resources and knowledge, we need to translate these materials into Chinese. Two preprocessing approaches, different in the timing of translation, were taken. One was to translate Japanese sentences in to Chinese ones, an...

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, or the U.S. government. We are grateful to Alex Rudnicky from CMU for providing the training data to us and to Yifen Huang, CMU, for discussing the project with us. Abstract A variety o...

Introduction and Overview: The topic of this dissertation is morphosyntactic part of speech tagging (abbreviated POS tagging) for Arabic. This topic has long and rich history for other languages, mainly for English. POS Tagging provides fundamental information about word forms used in sentences of natural language. The method of utilizing this information varies depending on the particular NLP ...

We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: p...