- BrillTagger class is a transformation-based tagger. It is not a subclass of SequentialBackoffTagger.
- Moreover, it uses a series of rules to correct the results of an initial tagger.
- These rules it follows are scored based. This score is equal to the no. of errors they correct minus the no. of new errors they produce.
Code #1 : Training a BrillTagger class
Code #2 : Let’s use the trained BrillTagger
Accuracy of Initial Tag : 0.8806820634578028
Code #3 :
Accuracy of brill_tag : 0.8827541549751781
- NLP | Training Unigram Tagger
- NLP | Training Tagger Based Chunker | Set 1
- NLP | Training Tagger Based Chunker | Set 2
- NLP | Classifier-based Chunking | Set 2
- Processing text using NLP | Basics
- Readability Index in Python(NLP)
- Feature Extraction Techniques - NLP
- Python | NLP analysis of Restaurant reviews
- Applying Multinomial Naive Bayes to NLP Problems
- NLP | Chunking and chinking with RegEx
- NLP | Synsets for a word in WordNet
- NLP | Part of Speech - Default Tagging
- NLP | Word Collocations
- NLP | WuPalmer - WordNet Similarity
- NLP | Training a tokenizer and filtering stopwords in a sentence
- NLP | How tokenizing text, sentence, words works
- NLP | Splitting and Merging Chunks
- NLP | Chunking Rules
- NLP | Expanding and Removing Chunks with RegEx
- NLP | Leacock Chordorow (LCH) and Path similarity for Synset
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.