Source code for nltk.tbl.rule

# -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Marcus Uneson <marcus.uneson@gmail.com>
#   based on previous (nltk2) version by
#   Christopher Maloof, Edward Loper, Steven Bird
# URL: <http://nltk.org/>
# For license information, see  LICENSE.TXT

from __future__ import print_function

from nltk.compat import python_2_unicode_compatible, unicode_repr
from nltk import jsontags

######################################################################
# Tag Rules
######################################################################


[docs]class TagRule(object): """ An interface for tag transformations on a tagged corpus, as performed by tbl taggers. Each transformation finds all tokens in the corpus that are tagged with a specific original tag and satisfy a specific condition, and replaces their tags with a replacement tag. For any given transformation, the original tag, replacement tag, and condition are fixed. Conditions may depend on the token under consideration, as well as any other tokens in the corpus. Tag rules must be comparable and hashable. """ def __init__(self, original_tag, replacement_tag): self.original_tag = original_tag """The tag which this TagRule may cause to be replaced.""" self.replacement_tag = replacement_tag """The tag with which this TagRule may replace another tag."""
[docs] def apply(self, tokens, positions=None): """ Apply this rule at every position in positions where it applies to the given sentence. I.e., for each position p in *positions*, if *tokens[p]* is tagged with this rule's original tag, and satisfies this rule's condition, then set its tag to be this rule's replacement tag. :param tokens: The tagged sentence :type tokens: list(tuple(str, str)) :type positions: list(int) :param positions: The positions where the transformation is to be tried. If not specified, try it at all positions. :return: The indices of tokens whose tags were changed by this rule. :rtype: int """ if positions is None: positions = list(range(len(tokens))) # Determine the indices at which this rule applies. change = [i for i in positions if self.applies(tokens, i)] # Make the changes. Note: this must be done in a separate # step from finding applicable locations, since we don't want # the rule to interact with itself. for i in change: tokens[i] = (tokens[i][0], self.replacement_tag) return change
[docs] def applies(self, tokens, index): """ :return: True if the rule would change the tag of ``tokens[index]``, False otherwise :rtype: bool :param tokens: A tagged sentence :type tokens: list(str) :param index: The index to check :type index: int """ raise NotImplementedError
# Rules must be comparable and hashable for the algorithm to work def __eq__(self, other): raise TypeError("Rules must implement __eq__()") def __ne__(self, other): raise TypeError("Rules must implement __ne__()") def __hash__(self): raise TypeError("Rules must implement __hash__()")
@python_2_unicode_compatible @jsontags.register_tag
[docs]class Rule(TagRule): """ A Rule checks the current corpus position for a certain set of conditions; if they are all fulfilled, the Rule is triggered, meaning that it will change tag A to tag B. For other tags than A, nothing happens. The conditions are parameters to the Rule instance. Each condition is a feature-value pair, with a set of positions to check for the value of the corresponding feature. Conceptually, the positions are joined by logical OR, and the feature set by logical AND. More formally, the Rule is then applicable to the M{n}th token iff: - The M{n}th token is tagged with the Rule's original tag; and - For each (Feature(positions), M{value}) tuple: - The value of Feature of at least one token in {n+p for p in positions} is M{value}. """ json_tag = 'nltk.tbl.Rule' def __init__(self, templateid, original_tag, replacement_tag, conditions): """ Construct a new Rule that changes a token's tag from C{original_tag} to C{replacement_tag} if all of the properties specified in C{conditions} hold. @type templateid: string @param templateid: the template id (a zero-padded string, '001' etc, so it will sort nicely) @type conditions: C{iterable} of C{Feature} @param conditions: A list of Feature(positions), each of which specifies that the property (computed by Feature.extract_property()) of at least one token in M{n} + p in positions is C{value}. """ TagRule.__init__(self, original_tag, replacement_tag) self._conditions = conditions self.templateid = templateid
[docs] def encode_json_obj(self): return { 'templateid': self.templateid, 'original': self.original_tag, 'replacement': self.replacement_tag, 'conditions': self._conditions, }
@classmethod
[docs] def decode_json_obj(cls, obj): return cls(obj['templateid'], obj['original'], obj['replacement'], obj['conditions'])
[docs] def applies(self, tokens, index): # Inherit docs from TagRule # Does the given token have this Rule's "original tag"? if tokens[index][1] != self.original_tag: return False # Check to make sure that every condition holds. for (feature, val) in self._conditions: # Look for *any* token that satisfies the condition. for pos in feature.positions: if not (0 <= index + pos < len(tokens)): continue if feature.extract_property(tokens, index+pos) == val: break else: # No token satisfied the condition; return false. return False # Every condition checked out, so the Rule is applicable. return True
def __eq__(self, other): return (self is other or (other is not None and other.__class__ == self.__class__ and self.original_tag == other.original_tag and self.replacement_tag == other.replacement_tag and self._conditions == other._conditions)) def __ne__(self, other): return not (self == other) def __hash__(self): # Cache our hash value (justified by profiling.) try: return self.__hash except AttributeError: self.__hash = hash(repr(self)) return self.__hash def __repr__(self): # Cache the repr (justified by profiling -- this is used as # a sort key when deterministic=True.) try: return self.__repr except AttributeError: self.__repr = ( "{0}('{1}', {2}, {3}, [{4}])".format( self.__class__.__name__, self.templateid, unicode_repr(self.original_tag), unicode_repr(self.replacement_tag), # list(self._conditions) would be simpler but will not generate # the same Rule.__repr__ in python 2 and 3 and thus break some tests ', '.join("({0},{1})".format(f, unicode_repr(v)) for (f, v) in self._conditions) ) ) return self.__repr def __str__(self): def _condition_to_logic(feature, value): """ Return a compact, predicate-logic styled string representation of the given condition. """ return '{0}:{1}@[{2}]'.format( feature.PROPERTY_NAME, value, ",".join(str(w) for w in feature.positions) ) conditions = ' & '.join([_condition_to_logic(f, v) for (f, v) in self._conditions]) s = '{0}->{1} if {2}'.format( self.original_tag, self.replacement_tag, conditions ) return s
[docs] def format(self, fmt): """ Return a string representation of this rule. >>> from nltk.tbl.rule import Rule >>> from nltk.tag.brill import Pos >>> r = Rule("23", "VB", "NN", [(Pos([-2,-1]), 'DT')]) r.format("str") == str(r) True >>> r.format("str") 'VB->NN if Pos:DT@[-2,-1]' r.format("repr") == repr(r) True >>> r.format("repr") "Rule('23', 'VB', 'NN', [(Pos([-2, -1]),'DT')])" >>> r.format("verbose") 'VB -> NN if the Pos of words i-2...i-1 is "DT"' >>> r.format("not_found") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "nltk/tbl/rule.py", line 256, in format raise ValueError("unknown rule format spec: {0}".format(fmt)) ValueError: unknown rule format spec: not_found >>> :param fmt: format specification :type fmt: str :return: string representation :rtype: str """ if fmt == "str": return self.__str__() elif fmt == "repr": return self.__repr__() elif fmt == "verbose": return self._verbose_format() else: raise ValueError("unknown rule format spec: {0}".format(fmt))
def _verbose_format(self): """ Return a wordy, human-readable string representation of the given rule. Not sure how useful this is. """ def condition_to_str(feature, value): return ('the %s of %s is "%s"' % (feature.PROPERTY_NAME, range_to_str(feature.positions), value)) def range_to_str(positions): if len(positions) == 1: p = positions[0] if p == 0: return 'this word' if p == -1: return 'the preceding word' elif p == 1: return 'the following word' elif p < 0: return 'word i-%d' % -p elif p > 0: return 'word i+%d' % p else: # for complete compatibility with the wordy format of nltk2 mx = max(positions) mn = min(positions) if mx - mn == len(positions) - 1: return 'words i%+d...i%+d' % (mn, mx) else: return 'words {%s}' % (",".join("i%+d" % d for d in positions),) replacement = '%s -> %s' % (self.original_tag, self.replacement_tag) conditions = (' if ' if self._conditions else "") + ', and '.join( condition_to_str(f, v) for (f, v) in self._conditions ) return replacement + conditions