Morphological features help POS tagging of unknown words across language varieties
Part-of-speech tagging, like any supervised statistical NLP task, is more difficult when test sets are very different from training sets, for example when tagging across genres or language varieties. We examined the problem of POS tagging of different varieties of Mandarin Chinese (PRC-Mainland, PRCHong Kong, and Taiwan). An analytic study first showed that unknown words were a major source of difficulty in cross-variety tagging. Unknown words in English tend to be proper nouns. By contrast, we found that Mandarin unknown words were mostly common nouns and verbs. We showed these results are caused by the high frequency of morphological compounding in Mandarin; in this sense Mandarin is more like German than English. Based on this analysis, we propose a variety of new morphological unknown-word features for POS tagging, extending earlier work by others on unknown-word tagging in English and German. Our features were implemented in a maximum entropy Markov model. Our system achieves state-of-the-art performance in Mandarin tagging, including improving unknown-word tagging performance on unseen varieties in Chinese Treebank 5.0 from 61% to 80% correct.