Class NgramTagger

Tags using an ngram model backed by NLTK.

Parameters:

• original_input - the input in its original, unprocessed form. This will usually be a string. This is optional, but in some circumstances things might fall apart if it hasn't been given. E.g. using a backoff model as backoff from a tagging model requires the original input to be passed to the backoff model.
• logger - optional progress logger. Logging will be sent to this during initialization of the tagger and tagging. If not given, the logging will be lost. Subclasses may access the logger (or a dummy logger if none was given) in self.logger.

Overrides: object.__init__

Returns a list of tuples (start, end, signtup). These represent spans to be added to the chart, start and end being the start and end nodes.

Each signtup is a (sign,tag,probability) tuple representing a sign that the tagger wishes to add to the chart in this position. How many are returned is up to the tagger (it may wish to return more in cases where there are no clear winners, for example). If the tag is not found in the grammar, sign will be None.

Returned list is sorted by probability, highest first.

offset may be set >0 in order to retrieve further signs once some have already been returned. If offset=k, the tagger should disregard all the signs that would have been returned for offset<k and return the next bunch - as many as it sees fit. offset is incremented each time the parse fails.

The simplest approach, and that employed by most taggers, has some signs for each word and none spanning more than one word. That is, the tuples in the list would be of the form (wordnum, wordnum+1, signtup). This is by no means required, though: some taggers will want to add multi-node spans to the chart.

Overrides: tagger.Tagger.get_signs

(inherited documentation)

Returns the input word at this index. This does not need to be a string, but must have a sensible __str__, so that it can be converted to a readable string. The purpose of this is to provide a readable form of the input for the parser to store in derivation traces.

Overrides: tagger.Tagger.get_word

(inherited documentation)

TAGGER_OPTIONS

Tagger-specific options. List of ModuleOptions.

Value:

ModelTagger.TAGGER_OPTIONS+ [ModuleOption('decode', filter= choose_fro m_list(DECODERS), help_text= "Decoding method for inference.", usage= "decode=X, where X is one of %s" % ", ".join("'%s'" % d for d in DECOD ERS), default= "forward-backward"),]