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- W2952890717 abstract "We present an algorithm that automatically learns context constraints using statistical decision trees. We then use the acquired constraints in a flexible POS tagger. The tagger is able to use information of any degree: n-grams, automatically learned context constraints, linguistically motivated manually written constraints, etc. The sources and kinds of constraints are unrestricted, and the language model can be easily extended, improving the results. The tagger has been tested and evaluated on the WSJ corpus." @default.
- W2952890717 created "2019-06-27" @default.
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- W2952890717 modified "2023-09-27" @default.
- W2952890717 title "A Flexible POS tagger Using an Automatically Acquired Language Model" @default.
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