329 lines
11 KiB
Python
329 lines
11 KiB
Python
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# -*- coding: utf-8 -*-
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# Natural Language Toolkit: Transformation-based learning
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Marcus Uneson <marcus.uneson@gmail.com>
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# based on previous (nltk2) version by
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# Christopher Maloof, Edward Loper, Steven Bird
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import print_function
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from abc import ABCMeta, abstractmethod
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from six import add_metaclass
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from nltk.compat import python_2_unicode_compatible, unicode_repr
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from nltk import jsontags
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######################################################################
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# Tag Rules
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######################################################################
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@add_metaclass(ABCMeta)
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class TagRule(object):
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"""
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An interface for tag transformations on a tagged corpus, as
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performed by tbl taggers. Each transformation finds all tokens
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in the corpus that are tagged with a specific original tag and
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satisfy a specific condition, and replaces their tags with a
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replacement tag. For any given transformation, the original
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tag, replacement tag, and condition are fixed. Conditions may
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depend on the token under consideration, as well as any other
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tokens in the corpus.
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Tag rules must be comparable and hashable.
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"""
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def __init__(self, original_tag, replacement_tag):
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self.original_tag = original_tag
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"""The tag which this TagRule may cause to be replaced."""
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self.replacement_tag = replacement_tag
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"""The tag with which this TagRule may replace another tag."""
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def apply(self, tokens, positions=None):
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"""
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Apply this rule at every position in positions where it
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applies to the given sentence. I.e., for each position p
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in *positions*, if *tokens[p]* is tagged with this rule's
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original tag, and satisfies this rule's condition, then set
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its tag to be this rule's replacement tag.
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:param tokens: The tagged sentence
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:type tokens: list(tuple(str, str))
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:type positions: list(int)
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:param positions: The positions where the transformation is to
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be tried. If not specified, try it at all positions.
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:return: The indices of tokens whose tags were changed by this
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rule.
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:rtype: int
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"""
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if positions is None:
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positions = list(range(len(tokens)))
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# Determine the indices at which this rule applies.
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change = [i for i in positions if self.applies(tokens, i)]
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# Make the changes. Note: this must be done in a separate
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# step from finding applicable locations, since we don't want
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# the rule to interact with itself.
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for i in change:
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tokens[i] = (tokens[i][0], self.replacement_tag)
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return change
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@abstractmethod
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def applies(self, tokens, index):
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"""
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:return: True if the rule would change the tag of
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``tokens[index]``, False otherwise
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:rtype: bool
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:param tokens: A tagged sentence
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:type tokens: list(str)
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:param index: The index to check
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:type index: int
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"""
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# Rules must be comparable and hashable for the algorithm to work
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def __eq__(self, other):
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raise TypeError("Rules must implement __eq__()")
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def __ne__(self, other):
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raise TypeError("Rules must implement __ne__()")
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def __hash__(self):
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raise TypeError("Rules must implement __hash__()")
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@python_2_unicode_compatible
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@jsontags.register_tag
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class Rule(TagRule):
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"""
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A Rule checks the current corpus position for a certain set of conditions;
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if they are all fulfilled, the Rule is triggered, meaning that it
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will change tag A to tag B. For other tags than A, nothing happens.
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The conditions are parameters to the Rule instance. Each condition is a feature-value pair,
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with a set of positions to check for the value of the corresponding feature.
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Conceptually, the positions are joined by logical OR, and the feature set by logical AND.
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More formally, the Rule is then applicable to the M{n}th token iff:
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- The M{n}th token is tagged with the Rule's original tag; and
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- For each (Feature(positions), M{value}) tuple:
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- The value of Feature of at least one token in {n+p for p in positions}
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is M{value}.
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"""
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json_tag = 'nltk.tbl.Rule'
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def __init__(self, templateid, original_tag, replacement_tag, conditions):
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"""
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Construct a new Rule that changes a token's tag from
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C{original_tag} to C{replacement_tag} if all of the properties
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specified in C{conditions} hold.
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@type templateid: string
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@param templateid: the template id (a zero-padded string, '001' etc,
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so it will sort nicely)
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@type conditions: C{iterable} of C{Feature}
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@param conditions: A list of Feature(positions),
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each of which specifies that the property (computed by
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Feature.extract_property()) of at least one
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token in M{n} + p in positions is C{value}.
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"""
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TagRule.__init__(self, original_tag, replacement_tag)
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self._conditions = conditions
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self.templateid = templateid
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def encode_json_obj(self):
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return {
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'templateid': self.templateid,
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'original': self.original_tag,
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'replacement': self.replacement_tag,
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'conditions': self._conditions,
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}
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@classmethod
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def decode_json_obj(cls, obj):
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return cls(
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obj['templateid'], obj['original'], obj['replacement'], obj['conditions']
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)
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def applies(self, tokens, index):
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# Inherit docs from TagRule
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# Does the given token have this Rule's "original tag"?
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if tokens[index][1] != self.original_tag:
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return False
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# Check to make sure that every condition holds.
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for (feature, val) in self._conditions:
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# Look for *any* token that satisfies the condition.
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for pos in feature.positions:
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if not (0 <= index + pos < len(tokens)):
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continue
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if feature.extract_property(tokens, index + pos) == val:
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break
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else:
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# No token satisfied the condition; return false.
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return False
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# Every condition checked out, so the Rule is applicable.
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return True
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def __eq__(self, other):
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return self is other or (
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other is not None
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and other.__class__ == self.__class__
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and self.original_tag == other.original_tag
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and self.replacement_tag == other.replacement_tag
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and self._conditions == other._conditions
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)
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def __ne__(self, other):
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return not (self == other)
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def __hash__(self):
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# Cache our hash value (justified by profiling.)
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try:
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return self.__hash
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except AttributeError:
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self.__hash = hash(repr(self))
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return self.__hash
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def __repr__(self):
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# Cache the repr (justified by profiling -- this is used as
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# a sort key when deterministic=True.)
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try:
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return self.__repr
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except AttributeError:
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self.__repr = "{0}('{1}', {2}, {3}, [{4}])".format(
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self.__class__.__name__,
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self.templateid,
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unicode_repr(self.original_tag),
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unicode_repr(self.replacement_tag),
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# list(self._conditions) would be simpler but will not generate
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# the same Rule.__repr__ in python 2 and 3 and thus break some tests
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', '.join(
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"({0},{1})".format(f, unicode_repr(v))
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for (f, v) in self._conditions
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),
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)
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return self.__repr
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def __str__(self):
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def _condition_to_logic(feature, value):
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"""
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Return a compact, predicate-logic styled string representation
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of the given condition.
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"""
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return '{0}:{1}@[{2}]'.format(
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feature.PROPERTY_NAME,
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value,
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",".join(str(w) for w in feature.positions),
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)
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conditions = ' & '.join(
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[_condition_to_logic(f, v) for (f, v) in self._conditions]
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)
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s = '{0}->{1} if {2}'.format(
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self.original_tag, self.replacement_tag, conditions
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)
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return s
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def format(self, fmt):
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"""
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Return a string representation of this rule.
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>>> from nltk.tbl.rule import Rule
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>>> from nltk.tag.brill import Pos
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>>> r = Rule("23", "VB", "NN", [(Pos([-2,-1]), 'DT')])
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r.format("str") == str(r)
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True
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>>> r.format("str")
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'VB->NN if Pos:DT@[-2,-1]'
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r.format("repr") == repr(r)
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True
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>>> r.format("repr")
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"Rule('23', 'VB', 'NN', [(Pos([-2, -1]),'DT')])"
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>>> r.format("verbose")
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'VB -> NN if the Pos of words i-2...i-1 is "DT"'
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>>> r.format("not_found")
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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File "nltk/tbl/rule.py", line 256, in format
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raise ValueError("unknown rule format spec: {0}".format(fmt))
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ValueError: unknown rule format spec: not_found
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>>>
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:param fmt: format specification
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:type fmt: str
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:return: string representation
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:rtype: str
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"""
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if fmt == "str":
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return self.__str__()
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elif fmt == "repr":
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return self.__repr__()
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elif fmt == "verbose":
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return self._verbose_format()
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else:
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raise ValueError("unknown rule format spec: {0}".format(fmt))
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def _verbose_format(self):
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"""
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Return a wordy, human-readable string representation
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of the given rule.
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Not sure how useful this is.
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"""
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def condition_to_str(feature, value):
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return 'the %s of %s is "%s"' % (
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feature.PROPERTY_NAME,
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range_to_str(feature.positions),
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value,
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)
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def range_to_str(positions):
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if len(positions) == 1:
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p = positions[0]
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if p == 0:
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return 'this word'
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if p == -1:
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return 'the preceding word'
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elif p == 1:
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return 'the following word'
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elif p < 0:
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return 'word i-%d' % -p
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elif p > 0:
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return 'word i+%d' % p
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else:
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# for complete compatibility with the wordy format of nltk2
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mx = max(positions)
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mn = min(positions)
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if mx - mn == len(positions) - 1:
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return 'words i%+d...i%+d' % (mn, mx)
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else:
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return 'words {%s}' % (",".join("i%+d" % d for d in positions),)
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replacement = '%s -> %s' % (self.original_tag, self.replacement_tag)
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conditions = (' if ' if self._conditions else "") + ', and '.join(
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condition_to_str(f, v) for (f, v) in self._conditions
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)
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return replacement + conditions
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