Factorial Hidden Markov Models for full and weakly supervised supertagging
For many sequence prediction tasks in Natural Language Processing, modeling dependencies between individual predictions can be used to improve prediction accuracy of the sequence as a whole. Supertagging, involves assigning lexical entries to words based on lexicalized grammatical theory such as Com...
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| Format: | Others |
| Language: | English |
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2010
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| Online Access: | http://hdl.handle.net/2152/ETD-UT-2009-08-350 |