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645 lines
20 KiB
Python
645 lines
20 KiB
Python
# Natural Language Toolkit: Chunk format conversions
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#
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# Copyright (C) 2001-2020 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# Steven Bird <stevenbird1@gmail.com> (minor additions)
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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import re
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from nltk.tree import Tree
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from nltk.tag.mapping import map_tag
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from nltk.tag.util import str2tuple
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##//////////////////////////////////////////////////////
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## EVALUATION
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##//////////////////////////////////////////////////////
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from nltk.metrics import accuracy as _accuracy
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def accuracy(chunker, gold):
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"""
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Score the accuracy of the chunker against the gold standard.
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Strip the chunk information from the gold standard and rechunk it using
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the chunker, then compute the accuracy score.
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:type chunker: ChunkParserI
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:param chunker: The chunker being evaluated.
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:type gold: tree
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:param gold: The chunk structures to score the chunker on.
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:rtype: float
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"""
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gold_tags = []
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test_tags = []
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for gold_tree in gold:
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test_tree = chunker.parse(gold_tree.flatten())
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gold_tags += tree2conlltags(gold_tree)
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test_tags += tree2conlltags(test_tree)
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# print 'GOLD:', gold_tags[:50]
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# print 'TEST:', test_tags[:50]
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return _accuracy(gold_tags, test_tags)
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# Patched for increased performance by Yoav Goldberg <yoavg@cs.bgu.ac.il>, 2006-01-13
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# -- statistics are evaluated only on demand, instead of at every sentence evaluation
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#
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# SB: use nltk.metrics for precision/recall scoring?
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#
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class ChunkScore(object):
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"""
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A utility class for scoring chunk parsers. ``ChunkScore`` can
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evaluate a chunk parser's output, based on a number of statistics
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(precision, recall, f-measure, misssed chunks, incorrect chunks).
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It can also combine the scores from the parsing of multiple texts;
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this makes it significantly easier to evaluate a chunk parser that
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operates one sentence at a time.
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Texts are evaluated with the ``score`` method. The results of
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evaluation can be accessed via a number of accessor methods, such
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as ``precision`` and ``f_measure``. A typical use of the
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``ChunkScore`` class is::
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>>> chunkscore = ChunkScore() # doctest: +SKIP
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>>> for correct in correct_sentences: # doctest: +SKIP
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... guess = chunkparser.parse(correct.leaves()) # doctest: +SKIP
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... chunkscore.score(correct, guess) # doctest: +SKIP
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>>> print('F Measure:', chunkscore.f_measure()) # doctest: +SKIP
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F Measure: 0.823
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:ivar kwargs: Keyword arguments:
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- max_tp_examples: The maximum number actual examples of true
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positives to record. This affects the ``correct`` member
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function: ``correct`` will not return more than this number
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of true positive examples. This does *not* affect any of
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the numerical metrics (precision, recall, or f-measure)
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- max_fp_examples: The maximum number actual examples of false
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positives to record. This affects the ``incorrect`` member
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function and the ``guessed`` member function: ``incorrect``
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will not return more than this number of examples, and
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``guessed`` will not return more than this number of true
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positive examples. This does *not* affect any of the
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numerical metrics (precision, recall, or f-measure)
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- max_fn_examples: The maximum number actual examples of false
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negatives to record. This affects the ``missed`` member
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function and the ``correct`` member function: ``missed``
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will not return more than this number of examples, and
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``correct`` will not return more than this number of true
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negative examples. This does *not* affect any of the
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numerical metrics (precision, recall, or f-measure)
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- chunk_label: A regular expression indicating which chunks
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should be compared. Defaults to ``'.*'`` (i.e., all chunks).
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:type _tp: list(Token)
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:ivar _tp: List of true positives
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:type _fp: list(Token)
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:ivar _fp: List of false positives
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:type _fn: list(Token)
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:ivar _fn: List of false negatives
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:type _tp_num: int
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:ivar _tp_num: Number of true positives
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:type _fp_num: int
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:ivar _fp_num: Number of false positives
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:type _fn_num: int
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:ivar _fn_num: Number of false negatives.
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"""
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def __init__(self, **kwargs):
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self._correct = set()
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self._guessed = set()
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self._tp = set()
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self._fp = set()
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self._fn = set()
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self._max_tp = kwargs.get("max_tp_examples", 100)
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self._max_fp = kwargs.get("max_fp_examples", 100)
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self._max_fn = kwargs.get("max_fn_examples", 100)
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self._chunk_label = kwargs.get("chunk_label", ".*")
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self._tp_num = 0
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self._fp_num = 0
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self._fn_num = 0
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self._count = 0
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self._tags_correct = 0.0
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self._tags_total = 0.0
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self._measuresNeedUpdate = False
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def _updateMeasures(self):
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if self._measuresNeedUpdate:
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self._tp = self._guessed & self._correct
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self._fn = self._correct - self._guessed
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self._fp = self._guessed - self._correct
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self._tp_num = len(self._tp)
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self._fp_num = len(self._fp)
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self._fn_num = len(self._fn)
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self._measuresNeedUpdate = False
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def score(self, correct, guessed):
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"""
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Given a correctly chunked sentence, score another chunked
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version of the same sentence.
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:type correct: chunk structure
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:param correct: The known-correct ("gold standard") chunked
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sentence.
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:type guessed: chunk structure
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:param guessed: The chunked sentence to be scored.
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"""
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self._correct |= _chunksets(correct, self._count, self._chunk_label)
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self._guessed |= _chunksets(guessed, self._count, self._chunk_label)
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self._count += 1
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self._measuresNeedUpdate = True
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# Keep track of per-tag accuracy (if possible)
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try:
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correct_tags = tree2conlltags(correct)
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guessed_tags = tree2conlltags(guessed)
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except ValueError:
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# This exception case is for nested chunk structures,
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# where tree2conlltags will fail with a ValueError: "Tree
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# is too deeply nested to be printed in CoNLL format."
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correct_tags = guessed_tags = ()
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self._tags_total += len(correct_tags)
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self._tags_correct += sum(
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1 for (t, g) in zip(guessed_tags, correct_tags) if t == g
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)
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def accuracy(self):
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"""
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Return the overall tag-based accuracy for all text that have
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been scored by this ``ChunkScore``, using the IOB (conll2000)
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tag encoding.
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:rtype: float
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"""
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if self._tags_total == 0:
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return 1
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return self._tags_correct / self._tags_total
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def precision(self):
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"""
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Return the overall precision for all texts that have been
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scored by this ``ChunkScore``.
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:rtype: float
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"""
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self._updateMeasures()
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div = self._tp_num + self._fp_num
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if div == 0:
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return 0
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else:
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return self._tp_num / div
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def recall(self):
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"""
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Return the overall recall for all texts that have been
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scored by this ``ChunkScore``.
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:rtype: float
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"""
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self._updateMeasures()
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div = self._tp_num + self._fn_num
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if div == 0:
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return 0
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else:
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return self._tp_num / div
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def f_measure(self, alpha=0.5):
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"""
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Return the overall F measure for all texts that have been
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scored by this ``ChunkScore``.
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:param alpha: the relative weighting of precision and recall.
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Larger alpha biases the score towards the precision value,
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while smaller alpha biases the score towards the recall
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value. ``alpha`` should have a value in the range [0,1].
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:type alpha: float
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:rtype: float
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"""
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self._updateMeasures()
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p = self.precision()
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r = self.recall()
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if p == 0 or r == 0: # what if alpha is 0 or 1?
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return 0
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return 1 / (alpha / p + (1 - alpha) / r)
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def missed(self):
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"""
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Return the chunks which were included in the
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correct chunk structures, but not in the guessed chunk
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structures, listed in input order.
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:rtype: list of chunks
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"""
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self._updateMeasures()
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chunks = list(self._fn)
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return [c[1] for c in chunks] # discard position information
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def incorrect(self):
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"""
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Return the chunks which were included in the guessed chunk structures,
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but not in the correct chunk structures, listed in input order.
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:rtype: list of chunks
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"""
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self._updateMeasures()
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chunks = list(self._fp)
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return [c[1] for c in chunks] # discard position information
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def correct(self):
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"""
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Return the chunks which were included in the correct
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chunk structures, listed in input order.
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:rtype: list of chunks
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"""
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chunks = list(self._correct)
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return [c[1] for c in chunks] # discard position information
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def guessed(self):
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"""
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Return the chunks which were included in the guessed
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chunk structures, listed in input order.
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:rtype: list of chunks
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"""
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chunks = list(self._guessed)
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return [c[1] for c in chunks] # discard position information
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def __len__(self):
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self._updateMeasures()
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return self._tp_num + self._fn_num
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def __repr__(self):
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"""
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Return a concise representation of this ``ChunkScoring``.
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:rtype: str
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"""
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return "<ChunkScoring of " + repr(len(self)) + " chunks>"
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def __str__(self):
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"""
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Return a verbose representation of this ``ChunkScoring``.
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This representation includes the precision, recall, and
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f-measure scores. For other information about the score,
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use the accessor methods (e.g., ``missed()`` and ``incorrect()``).
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:rtype: str
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"""
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return (
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"ChunkParse score:\n"
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+ (" IOB Accuracy: {:5.1f}%%\n".format(self.accuracy() * 100))
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+ (" Precision: {:5.1f}%%\n".format(self.precision() * 100))
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+ (" Recall: {:5.1f}%%\n".format(self.recall() * 100))
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+ (" F-Measure: {:5.1f}%%".format(self.f_measure() * 100))
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)
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# extract chunks, and assign unique id, the absolute position of
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# the first word of the chunk
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def _chunksets(t, count, chunk_label):
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pos = 0
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chunks = []
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for child in t:
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if isinstance(child, Tree):
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if re.match(chunk_label, child.label()):
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chunks.append(((count, pos), child.freeze()))
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pos += len(child.leaves())
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else:
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pos += 1
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return set(chunks)
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def tagstr2tree(
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s, chunk_label="NP", root_label="S", sep="/", source_tagset=None, target_tagset=None
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):
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"""
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Divide a string of bracketted tagged text into
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chunks and unchunked tokens, and produce a Tree.
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Chunks are marked by square brackets (``[...]``). Words are
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delimited by whitespace, and each word should have the form
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``text/tag``. Words that do not contain a slash are
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assigned a ``tag`` of None.
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:param s: The string to be converted
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:type s: str
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:param chunk_label: The label to use for chunk nodes
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:type chunk_label: str
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:param root_label: The label to use for the root of the tree
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:type root_label: str
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:rtype: Tree
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"""
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WORD_OR_BRACKET = re.compile(r"\[|\]|[^\[\]\s]+")
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stack = [Tree(root_label, [])]
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for match in WORD_OR_BRACKET.finditer(s):
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text = match.group()
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if text[0] == "[":
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if len(stack) != 1:
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raise ValueError("Unexpected [ at char {:d}".format(match.start()))
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chunk = Tree(chunk_label, [])
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stack[-1].append(chunk)
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stack.append(chunk)
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elif text[0] == "]":
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if len(stack) != 2:
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raise ValueError("Unexpected ] at char {:d}".format(match.start()))
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stack.pop()
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else:
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if sep is None:
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stack[-1].append(text)
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else:
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word, tag = str2tuple(text, sep)
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if source_tagset and target_tagset:
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tag = map_tag(source_tagset, target_tagset, tag)
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stack[-1].append((word, tag))
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if len(stack) != 1:
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raise ValueError("Expected ] at char {:d}".format(len(s)))
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return stack[0]
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### CONLL
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_LINE_RE = re.compile("(\S+)\s+(\S+)\s+([IOB])-?(\S+)?")
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def conllstr2tree(s, chunk_types=("NP", "PP", "VP"), root_label="S"):
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"""
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Return a chunk structure for a single sentence
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encoded in the given CONLL 2000 style string.
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This function converts a CoNLL IOB string into a tree.
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It uses the specified chunk types
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(defaults to NP, PP and VP), and creates a tree rooted at a node
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labeled S (by default).
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:param s: The CoNLL string to be converted.
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:type s: str
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:param chunk_types: The chunk types to be converted.
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:type chunk_types: tuple
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:param root_label: The node label to use for the root.
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:type root_label: str
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:rtype: Tree
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"""
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stack = [Tree(root_label, [])]
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for lineno, line in enumerate(s.split("\n")):
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if not line.strip():
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continue
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# Decode the line.
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match = _LINE_RE.match(line)
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if match is None:
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raise ValueError("Error on line {:d}".format(lineno))
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(word, tag, state, chunk_type) = match.groups()
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# If it's a chunk type we don't care about, treat it as O.
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if chunk_types is not None and chunk_type not in chunk_types:
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state = "O"
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# For "Begin"/"Outside", finish any completed chunks -
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# also do so for "Inside" which don't match the previous token.
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mismatch_I = state == "I" and chunk_type != stack[-1].label()
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if state in "BO" or mismatch_I:
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if len(stack) == 2:
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stack.pop()
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# For "Begin", start a new chunk.
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if state == "B" or mismatch_I:
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chunk = Tree(chunk_type, [])
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stack[-1].append(chunk)
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stack.append(chunk)
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# Add the new word token.
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stack[-1].append((word, tag))
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return stack[0]
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def tree2conlltags(t):
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"""
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Return a list of 3-tuples containing ``(word, tag, IOB-tag)``.
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Convert a tree to the CoNLL IOB tag format.
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:param t: The tree to be converted.
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:type t: Tree
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:rtype: list(tuple)
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"""
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tags = []
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for child in t:
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try:
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category = child.label()
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prefix = "B-"
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for contents in child:
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if isinstance(contents, Tree):
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raise ValueError(
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"Tree is too deeply nested to be printed in CoNLL format"
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)
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tags.append((contents[0], contents[1], prefix + category))
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prefix = "I-"
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except AttributeError:
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tags.append((child[0], child[1], "O"))
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return tags
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def conlltags2tree(
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sentence, chunk_types=("NP", "PP", "VP"), root_label="S", strict=False
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):
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"""
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Convert the CoNLL IOB format to a tree.
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"""
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tree = Tree(root_label, [])
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for (word, postag, chunktag) in sentence:
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if chunktag is None:
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if strict:
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raise ValueError("Bad conll tag sequence")
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else:
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# Treat as O
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tree.append((word, postag))
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elif chunktag.startswith("B-"):
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tree.append(Tree(chunktag[2:], [(word, postag)]))
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elif chunktag.startswith("I-"):
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if (
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len(tree) == 0
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or not isinstance(tree[-1], Tree)
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or tree[-1].label() != chunktag[2:]
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):
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if strict:
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raise ValueError("Bad conll tag sequence")
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else:
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# Treat as B-*
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tree.append(Tree(chunktag[2:], [(word, postag)]))
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else:
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tree[-1].append((word, postag))
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elif chunktag == "O":
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tree.append((word, postag))
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else:
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raise ValueError("Bad conll tag {0!r}".format(chunktag))
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return tree
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def tree2conllstr(t):
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"""
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Return a multiline string where each line contains a word, tag and IOB tag.
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Convert a tree to the CoNLL IOB string format
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:param t: The tree to be converted.
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:type t: Tree
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:rtype: str
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"""
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lines = [" ".join(token) for token in tree2conlltags(t)]
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return "\n".join(lines)
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### IEER
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_IEER_DOC_RE = re.compile(
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r"<DOC>\s*"
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r"(<DOCNO>\s*(?P<docno>.+?)\s*</DOCNO>\s*)?"
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r"(<DOCTYPE>\s*(?P<doctype>.+?)\s*</DOCTYPE>\s*)?"
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r"(<DATE_TIME>\s*(?P<date_time>.+?)\s*</DATE_TIME>\s*)?"
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r"<BODY>\s*"
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r"(<HEADLINE>\s*(?P<headline>.+?)\s*</HEADLINE>\s*)?"
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r"<TEXT>(?P<text>.*?)</TEXT>\s*"
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r"</BODY>\s*</DOC>\s*",
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re.DOTALL,
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)
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_IEER_TYPE_RE = re.compile('<b_\w+\s+[^>]*?type="(?P<type>\w+)"')
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def _ieer_read_text(s, root_label):
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stack = [Tree(root_label, [])]
|
|
# s will be None if there is no headline in the text
|
|
# return the empty list in place of a Tree
|
|
if s is None:
|
|
return []
|
|
for piece_m in re.finditer("<[^>]+>|[^\s<]+", s):
|
|
piece = piece_m.group()
|
|
try:
|
|
if piece.startswith("<b_"):
|
|
m = _IEER_TYPE_RE.match(piece)
|
|
if m is None:
|
|
print("XXXX", piece)
|
|
chunk = Tree(m.group("type"), [])
|
|
stack[-1].append(chunk)
|
|
stack.append(chunk)
|
|
elif piece.startswith("<e_"):
|
|
stack.pop()
|
|
# elif piece.startswith('<'):
|
|
# print "ERROR:", piece
|
|
# raise ValueError # Unexpected HTML
|
|
else:
|
|
stack[-1].append(piece)
|
|
except (IndexError, ValueError):
|
|
raise ValueError(
|
|
"Bad IEER string (error at character {:d})".format(piece_m.start())
|
|
)
|
|
if len(stack) != 1:
|
|
raise ValueError("Bad IEER string")
|
|
return stack[0]
|
|
|
|
|
|
def ieerstr2tree(
|
|
s,
|
|
chunk_types=[
|
|
"LOCATION",
|
|
"ORGANIZATION",
|
|
"PERSON",
|
|
"DURATION",
|
|
"DATE",
|
|
"CARDINAL",
|
|
"PERCENT",
|
|
"MONEY",
|
|
"MEASURE",
|
|
],
|
|
root_label="S",
|
|
):
|
|
"""
|
|
Return a chunk structure containing the chunked tagged text that is
|
|
encoded in the given IEER style string.
|
|
Convert a string of chunked tagged text in the IEER named
|
|
entity format into a chunk structure. Chunks are of several
|
|
types, LOCATION, ORGANIZATION, PERSON, DURATION, DATE, CARDINAL,
|
|
PERCENT, MONEY, and MEASURE.
|
|
|
|
:rtype: Tree
|
|
"""
|
|
|
|
# Try looking for a single document. If that doesn't work, then just
|
|
# treat everything as if it was within the <TEXT>...</TEXT>.
|
|
m = _IEER_DOC_RE.match(s)
|
|
if m:
|
|
return {
|
|
"text": _ieer_read_text(m.group("text"), root_label),
|
|
"docno": m.group("docno"),
|
|
"doctype": m.group("doctype"),
|
|
"date_time": m.group("date_time"),
|
|
#'headline': m.group('headline')
|
|
# we want to capture NEs in the headline too!
|
|
"headline": _ieer_read_text(m.group("headline"), root_label),
|
|
}
|
|
else:
|
|
return _ieer_read_text(s, root_label)
|
|
|
|
|
|
def demo():
|
|
|
|
s = "[ Pierre/NNP Vinken/NNP ] ,/, [ 61/CD years/NNS ] old/JJ ,/, will/MD join/VB [ the/DT board/NN ] ./."
|
|
import nltk
|
|
|
|
t = nltk.chunk.tagstr2tree(s, chunk_label="NP")
|
|
t.pprint()
|
|
print()
|
|
|
|
s = """
|
|
These DT B-NP
|
|
research NN I-NP
|
|
protocols NNS I-NP
|
|
offer VBP B-VP
|
|
to TO B-PP
|
|
the DT B-NP
|
|
patient NN I-NP
|
|
not RB O
|
|
only RB O
|
|
the DT B-NP
|
|
very RB I-NP
|
|
best JJS I-NP
|
|
therapy NN I-NP
|
|
which WDT B-NP
|
|
we PRP B-NP
|
|
have VBP B-VP
|
|
established VBN I-VP
|
|
today NN B-NP
|
|
but CC B-NP
|
|
also RB I-NP
|
|
the DT B-NP
|
|
hope NN I-NP
|
|
of IN B-PP
|
|
something NN B-NP
|
|
still RB B-ADJP
|
|
better JJR I-ADJP
|
|
. . O
|
|
"""
|
|
|
|
conll_tree = conllstr2tree(s, chunk_types=("NP", "PP"))
|
|
conll_tree.pprint()
|
|
|
|
# Demonstrate CoNLL output
|
|
print("CoNLL output:")
|
|
print(nltk.chunk.tree2conllstr(conll_tree))
|
|
print()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
demo()
|