nltk.tag.sequential.BigramTagger

class documentation

class BigramTagger(NgramTagger): (source)

Constructor: BigramTagger(train, model, backoff, cutoff, verbose)

A tagger that chooses a token's tag based its word string and on the preceding words' tag. In particular, a tuple consisting of the previous tag and the word is looked up in a table, and the corresponding tag is returned.

Parameters
train	The corpus of training data, a list of tagged sentences
model	The tagger model
backoff	Another tagger which this tagger will consult when it is unable to tag a word
cutoff	The number of instances of training data the tagger must see in order not to use the backoff tagger

Method	`__init__`	No summary
Class Variable	`json_tag`	Undocumented

Inherited from NgramTagger:

Class Method	`decode_json_obj`	Undocumented
Method	`context`	No summary
Method	`encode_json_obj`	Undocumented
Instance Variable	`_n`	Undocumented

Inherited from ContextTagger (via NgramTagger):

Method	`__repr__`	Undocumented
Method	`choose_tag`	Decide which tag should be used for the specified token, and return that tag. If this tagger is unable to determine a tag for the specified token, return None -- do not consult the backoff tagger. This method should be overridden by subclasses of SequentialBackoffTagger.
Method	`size`	No summary
Method	`_train`	Initialize this ContextTagger's `_context_to_tag` table based on the given training data. In particular, for each context `c` in the training data, set `_context_to_tag[c]` to the most frequent tag for that context...
Instance Variable	`_context_to_tag`	Dictionary mapping contexts to tags.

Inherited from SequentialBackoffTagger (via NgramTagger, ContextTagger):

Method	`tag`	Determine the most appropriate tag sequence for the given token sequence, and return a corresponding list of tagged tokens. A tagged token is encoded as a tuple `(token, tag)`.
Method	`tag_one`	Determine an appropriate tag for the specified token, and return that tag. If this tagger is unable to determine a tag for the specified token, then its backoff tagger is consulted.
Instance Variable	`_taggers`	A list of all the taggers that should be tried to tag a token (i.e., self and its backoff taggers).

Inherited from TaggerI (via NgramTagger, ContextTagger, SequentialBackoffTagger):

Method	`evaluate`	Score the accuracy of the tagger against the gold standard. Strip the tags from the gold standard text, retag it using the tagger, then compute the accuracy score.
Method	`tag_sents`	Apply `self.tag()` to each element of sentences. I.e.:
Method	`_check_params`	Undocumented

def __init__(self, train=None, model=None, backoff=None, cutoff=0, verbose=False): (source) ¶

overrides nltk.tag.sequential.NgramTagger.__init__

Parameters
train	Undocumented
model	Undocumented
backoff	The backoff tagger that should be used for this tagger.
cutoff	Undocumented
verbose	Undocumented
context_to_tag	A dictionary mapping contexts to tags.

json_tag: str = (source) ¶

overrides nltk.tag.sequential.NgramTagger.json_tag

Undocumented