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To train your own taggers, see for example my µ-TBL system or the fnTBL toolkit. This package does not (yet) include a transformation-based learner. At least this is the result that Ramshaw and Marcus (1995) claim for this kind of chunker. Injected with more rules it can be expected to land just above 93%. The accuracy of the chunker is probably around 91-92 percent.
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95-97% of the word tokens in arbitrary English text receive the correct tag).
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The accuracy of the part-of-speech tagger should be around 95-97% (i.e.
Free mp3 tagger for mac how to#
The package includes two examples which show how to build a part-of-speech tagger for English, as well as a combined part-of-speech tagger and noun-phrase chunker, also for English. In particular, a derived class is expected to contain (or import) the rules by means of which the tagger will be operating and thus it encapsulates everything which is specific to a particular language and application. The tagger is an abstract class (in the sense that it does not define all the methods that it calls) and you will need to subclass it in order to do something useful. This is an implementation in pure Oz of a Brill-style rule-based tagger (Brill 1995). X-ozlib://lager/tb-tagger/chunk.exe requires x-ozlib://lager/sentence-splitter/SentenceSplitter.ozf x-ozlib://lager/simple-tokenizer/EnglishTokenizer.ozf Provides x-ozlib://lager/tb-tagger/Tagger.ozf x-ozlib://lager/tb-tagger/EnglishTagger.ozf x-ozlib://lager/tb-tagger/EnglishTaggerAndChunker.ozf