Understanding the concept —
DefaultTagger classto tag words that n-gram tag tags have missed, and thus can be a useful part of the rollback chain.
choose_tag (), it iterates over templates. It then returns the first tag of the expression, which can match the current word using re.match ().
Code # 1: Regular Expression Python Module and Repeated Syntax
The RegexpTagger class expects a list of two tuples
- & gt; first element in the tuple is a regular expression - & gt; second element is the tag
Code # 2: Using RegexpTagger
What is affix tagging?
This is a subclass of ContextTagger. In the case of the AffixTagger class, the context is either a suffix or a prefix of a word. Thus, it clearly indicates that this class can learn tags based on substrings of a fixed length of the beginning or end of a word.
Indicates three-character suffixes. These words must be at least 5 characters long, and None is returned as a tag if the word contains less than five characters.
Code # 3: Understanding AffixTagger.
Train data: [('Mr.',' NNP'), ('Vinken',' NNP'), ('is ',' VBZ'), ('chairman',' NN'), ('of',' IN'), ('Elsevier',' NNP'), ('NV',' NNP'), (', ',', '), (' the', 'DT'), (' Dutch', 'NNP'), (' publishing', 'VBG'), (' group', 'NN'), ('. ',' .')] Accuracy: 0.27558817181092166
Code # 4: AffixTagger, specifying 3-character prefixes.
Code # 5: AffixTagger with 2-character suffixes
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