Abstract
We present a web-based algorithm for the task of POS tagging of unknown words (words appearing only a small number of times in the training data of a supervised POS tagger). When a sentence s containing an unknown word u is to be tagged by a trained POS tagger, our algorithm collects from the web contexts that are partially similar to the context of u in s, which are then used to compute new tag assignment probabilities for u. Our algorithm enables fast multi-domain unknown word tagging, since, unlike previous work, it does not require a corpus from the new domain. We integrate our algorithm into the MXPOST POS tagger (Ratnaparkhi, 1996) and experiment with three languages (English, German and Chinese) in seven in-domain and domain adaptation scenarios. Our algorithm provides an error reduction of up to 15.63% (English), 18.09% (German) and 13.57% (Chinese) over the original tagger.
| Original language | English |
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| Pages | 1274-1282 |
| Number of pages | 9 |
| State | Published - 2010 |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: 23 Aug 2010 → 27 Aug 2010 |
Conference
| Conference | 23rd International Conference on Computational Linguistics, Coling 2010 |
|---|---|
| Country/Territory | China |
| City | Beijing |
| Period | 23/08/10 → 27/08/10 |