Develop a Part-of-Speech Tagger and a Tagger-Maker

Develop a Part-of-Speech Tagger and a Tagger-Maker


Marketed By :  LAP LAMBERT Academic Publishing   Sold By :  Kamal Books International  
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  • Product Description

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.

Product Specifications
SKU :COC65510
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-08
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