Develop a Part-of-Speech Tagger and a Tagger-Maker
Algorithms, Implementations, Results, and APIs
(Sprache: Englisch)
This project is aimed to build an efficient, scalable, portable, and trainable part-of-speech tagger. Using 98% of Penn Treebank-3 as the training data, it builds a raw tagger, using Bayes' theorem, a hidden Markov model, and the Viterbi algorithm. After...
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This project is aimed to build an efficient, scalable, portable, and trainable part-of-speech tagger. Using 98% of Penn Treebank-3 as the training data, it builds a raw tagger, using Bayes' theorem, a hidden Markov model, and the Viterbi algorithm. After that, a reinforcement machine learning algorithm and contextual transformation rules were applied to increase the tagger's accuracy. The tagger's final accuracy on the testing data is 96.51% and its speed is about 26,000 words per second on a computer with two-gigabyte random access memory and two 3.00 GHz Pentium duo processors. The tagger's portability and trainability are proved by the tagger-maker's success in building a new tagger out of a corpus that is annotated with the tagset different from that of Penn Treebank.
Bibliographische Angaben
- Autor: Jiayun Han
- 2013, 68 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659376221
- ISBN-13: 9783659376221
Sprache:
Englisch
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