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715 lines
25 KiB
715 lines
25 KiB
# Natural Language Toolkit: Texts
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Steven Bird <stevenbird1@gmail.com>
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# Edward Loper <edloper@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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This module brings together a variety of NLTK functionality for
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text analysis, and provides simple, interactive interfaces.
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Functionality includes: concordancing, collocation discovery,
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regular expression search over tokenized strings, and
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distributional similarity.
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"""
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from __future__ import print_function, division, unicode_literals, absolute_import
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from math import log
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from collections import defaultdict, Counter, namedtuple
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from functools import reduce
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import re
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from six import text_type
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from nltk.probability import FreqDist
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from nltk.probability import ConditionalFreqDist as CFD
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from nltk.util import tokenwrap, LazyConcatenation
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from nltk.metrics import f_measure, BigramAssocMeasures
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from nltk.collocations import BigramCollocationFinder
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from nltk.compat import python_2_unicode_compatible
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ConcordanceLine = namedtuple(
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'ConcordanceLine',
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['left', 'query', 'right', 'offset', 'left_print', 'right_print', 'line'],
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)
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class ContextIndex(object):
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"""
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A bidirectional index between words and their 'contexts' in a text.
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The context of a word is usually defined to be the words that occur
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in a fixed window around the word; but other definitions may also
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be used by providing a custom context function.
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"""
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@staticmethod
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def _default_context(tokens, i):
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"""One left token and one right token, normalized to lowercase"""
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left = tokens[i - 1].lower() if i != 0 else '*START*'
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right = tokens[i + 1].lower() if i != len(tokens) - 1 else '*END*'
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return (left, right)
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def __init__(self, tokens, context_func=None, filter=None, key=lambda x: x):
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self._key = key
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self._tokens = tokens
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if context_func:
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self._context_func = context_func
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else:
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self._context_func = self._default_context
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if filter:
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tokens = [t for t in tokens if filter(t)]
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self._word_to_contexts = CFD(
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(self._key(w), self._context_func(tokens, i)) for i, w in enumerate(tokens)
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)
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self._context_to_words = CFD(
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(self._context_func(tokens, i), self._key(w)) for i, w in enumerate(tokens)
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)
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def tokens(self):
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"""
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:rtype: list(str)
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:return: The document that this context index was
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created from.
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"""
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return self._tokens
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def word_similarity_dict(self, word):
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"""
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Return a dictionary mapping from words to 'similarity scores,'
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indicating how often these two words occur in the same
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context.
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"""
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word = self._key(word)
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word_contexts = set(self._word_to_contexts[word])
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scores = {}
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for w, w_contexts in self._word_to_contexts.items():
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scores[w] = f_measure(word_contexts, set(w_contexts))
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return scores
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def similar_words(self, word, n=20):
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scores = defaultdict(int)
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for c in self._word_to_contexts[self._key(word)]:
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for w in self._context_to_words[c]:
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if w != word:
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scores[w] += (
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self._context_to_words[c][word] * self._context_to_words[c][w]
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)
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return sorted(scores, key=scores.get, reverse=True)[:n]
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def common_contexts(self, words, fail_on_unknown=False):
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"""
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Find contexts where the specified words can all appear; and
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return a frequency distribution mapping each context to the
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number of times that context was used.
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:param words: The words used to seed the similarity search
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:type words: str
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:param fail_on_unknown: If true, then raise a value error if
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any of the given words do not occur at all in the index.
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"""
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words = [self._key(w) for w in words]
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contexts = [set(self._word_to_contexts[w]) for w in words]
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empty = [words[i] for i in range(len(words)) if not contexts[i]]
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common = reduce(set.intersection, contexts)
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if empty and fail_on_unknown:
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raise ValueError("The following word(s) were not found:", " ".join(words))
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elif not common:
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# nothing in common -- just return an empty freqdist.
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return FreqDist()
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else:
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fd = FreqDist(
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c for w in words for c in self._word_to_contexts[w] if c in common
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)
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return fd
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@python_2_unicode_compatible
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class ConcordanceIndex(object):
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"""
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An index that can be used to look up the offset locations at which
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a given word occurs in a document.
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"""
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def __init__(self, tokens, key=lambda x: x):
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"""
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Construct a new concordance index.
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:param tokens: The document (list of tokens) that this
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concordance index was created from. This list can be used
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to access the context of a given word occurrence.
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:param key: A function that maps each token to a normalized
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version that will be used as a key in the index. E.g., if
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you use ``key=lambda s:s.lower()``, then the index will be
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case-insensitive.
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"""
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self._tokens = tokens
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"""The document (list of tokens) that this concordance index
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was created from."""
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self._key = key
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"""Function mapping each token to an index key (or None)."""
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self._offsets = defaultdict(list)
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"""Dictionary mapping words (or keys) to lists of offset indices."""
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# Initialize the index (self._offsets)
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for index, word in enumerate(tokens):
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word = self._key(word)
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self._offsets[word].append(index)
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def tokens(self):
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"""
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:rtype: list(str)
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:return: The document that this concordance index was
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created from.
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"""
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return self._tokens
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def offsets(self, word):
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"""
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:rtype: list(int)
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:return: A list of the offset positions at which the given
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word occurs. If a key function was specified for the
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index, then given word's key will be looked up.
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"""
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word = self._key(word)
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return self._offsets[word]
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def __repr__(self):
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return '<ConcordanceIndex for %d tokens (%d types)>' % (
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len(self._tokens),
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len(self._offsets),
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)
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def find_concordance(self, word, width=80):
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"""
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Find all concordance lines given the query word.
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"""
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half_width = (width - len(word) - 2) // 2
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context = width // 4 # approx number of words of context
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# Find the instances of the word to create the ConcordanceLine
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concordance_list = []
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offsets = self.offsets(word)
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if offsets:
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for i in offsets:
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query_word = self._tokens[i]
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# Find the context of query word.
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left_context = self._tokens[max(0, i - context) : i]
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right_context = self._tokens[i + 1 : i + context]
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# Create the pretty lines with the query_word in the middle.
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left_print = ' '.join(left_context)[-half_width:]
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right_print = ' '.join(right_context)[:half_width]
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# The WYSIWYG line of the concordance.
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line_print = ' '.join([left_print, query_word, right_print])
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# Create the ConcordanceLine
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concordance_line = ConcordanceLine(
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left_context,
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query_word,
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right_context,
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i,
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left_print,
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right_print,
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line_print,
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)
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concordance_list.append(concordance_line)
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return concordance_list
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def print_concordance(self, word, width=80, lines=25):
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"""
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Print concordance lines given the query word.
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:param word: The target word
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:type word: str
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:param lines: The number of lines to display (default=25)
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:type lines: int
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:param width: The width of each line, in characters (default=80)
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:type width: int
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:param save: The option to save the concordance.
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:type save: bool
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"""
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concordance_list = self.find_concordance(word, width=width)
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if not concordance_list:
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print("no matches")
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else:
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lines = min(lines, len(concordance_list))
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print("Displaying {} of {} matches:".format(lines, len(concordance_list)))
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for i, concordance_line in enumerate(concordance_list[:lines]):
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print(concordance_line.line)
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class TokenSearcher(object):
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"""
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A class that makes it easier to use regular expressions to search
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over tokenized strings. The tokenized string is converted to a
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string where tokens are marked with angle brackets -- e.g.,
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``'<the><window><is><still><open>'``. The regular expression
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passed to the ``findall()`` method is modified to treat angle
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brackets as non-capturing parentheses, in addition to matching the
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token boundaries; and to have ``'.'`` not match the angle brackets.
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"""
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def __init__(self, tokens):
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self._raw = ''.join('<' + w + '>' for w in tokens)
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def findall(self, regexp):
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"""
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Find instances of the regular expression in the text.
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The text is a list of tokens, and a regexp pattern to match
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a single token must be surrounded by angle brackets. E.g.
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>>> from nltk.text import TokenSearcher
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>>> print('hack'); from nltk.book import text1, text5, text9
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hack...
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>>> text5.findall("<.*><.*><bro>")
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you rule bro; telling you bro; u twizted bro
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>>> text1.findall("<a>(<.*>)<man>")
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monied; nervous; dangerous; white; white; white; pious; queer; good;
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mature; white; Cape; great; wise; wise; butterless; white; fiendish;
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pale; furious; better; certain; complete; dismasted; younger; brave;
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brave; brave; brave
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>>> text9.findall("<th.*>{3,}")
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thread through those; the thought that; that the thing; the thing
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that; that that thing; through these than through; them that the;
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through the thick; them that they; thought that the
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:param regexp: A regular expression
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:type regexp: str
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"""
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# preprocess the regular expression
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regexp = re.sub(r'\s', '', regexp)
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regexp = re.sub(r'<', '(?:<(?:', regexp)
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regexp = re.sub(r'>', ')>)', regexp)
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regexp = re.sub(r'(?<!\\)\.', '[^>]', regexp)
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# perform the search
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hits = re.findall(regexp, self._raw)
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# Sanity check
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for h in hits:
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if not h.startswith('<') and h.endswith('>'):
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raise ValueError('Bad regexp for TokenSearcher.findall')
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# postprocess the output
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hits = [h[1:-1].split('><') for h in hits]
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return hits
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@python_2_unicode_compatible
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class Text(object):
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"""
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A wrapper around a sequence of simple (string) tokens, which is
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intended to support initial exploration of texts (via the
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interactive console). Its methods perform a variety of analyses
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on the text's contexts (e.g., counting, concordancing, collocation
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discovery), and display the results. If you wish to write a
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program which makes use of these analyses, then you should bypass
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the ``Text`` class, and use the appropriate analysis function or
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class directly instead.
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A ``Text`` is typically initialized from a given document or
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corpus. E.g.:
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>>> import nltk.corpus
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>>> from nltk.text import Text
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>>> moby = Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
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"""
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# This defeats lazy loading, but makes things faster. This
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# *shouldn't* be necessary because the corpus view *should* be
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# doing intelligent caching, but without this it's running slow.
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# Look into whether the caching is working correctly.
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_COPY_TOKENS = True
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def __init__(self, tokens, name=None):
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"""
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Create a Text object.
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:param tokens: The source text.
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:type tokens: sequence of str
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"""
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if self._COPY_TOKENS:
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tokens = list(tokens)
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self.tokens = tokens
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if name:
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self.name = name
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elif ']' in tokens[:20]:
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end = tokens[:20].index(']')
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self.name = " ".join(text_type(tok) for tok in tokens[1:end])
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else:
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self.name = " ".join(text_type(tok) for tok in tokens[:8]) + "..."
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# ////////////////////////////////////////////////////////////
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# Support item & slice access
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# ////////////////////////////////////////////////////////////
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def __getitem__(self, i):
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return self.tokens[i]
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def __len__(self):
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return len(self.tokens)
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# ////////////////////////////////////////////////////////////
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# Interactive console methods
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# ////////////////////////////////////////////////////////////
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def concordance(self, word, width=79, lines=25):
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"""
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Prints a concordance for ``word`` with the specified context window.
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Word matching is not case-sensitive.
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:param word: The target word
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:type word: str
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:param width: The width of each line, in characters (default=80)
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:type width: int
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:param lines: The number of lines to display (default=25)
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:type lines: int
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:seealso: ``ConcordanceIndex``
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"""
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if '_concordance_index' not in self.__dict__:
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self._concordance_index = ConcordanceIndex(
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self.tokens, key=lambda s: s.lower()
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)
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return self._concordance_index.print_concordance(word, width, lines)
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def concordance_list(self, word, width=79, lines=25):
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"""
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Generate a concordance for ``word`` with the specified context window.
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Word matching is not case-sensitive.
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:param word: The target word
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:type word: str
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:param width: The width of each line, in characters (default=80)
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:type width: int
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:param lines: The number of lines to display (default=25)
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:type lines: int
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:seealso: ``ConcordanceIndex``
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"""
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if '_concordance_index' not in self.__dict__:
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self._concordance_index = ConcordanceIndex(
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self.tokens, key=lambda s: s.lower()
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)
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return self._concordance_index.find_concordance(word, width)[:lines]
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def collocation_list(self, num=20, window_size=2):
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"""
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Return collocations derived from the text, ignoring stopwords.
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:param num: The maximum number of collocations to return.
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:type num: int
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:param window_size: The number of tokens spanned by a collocation (default=2)
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:type window_size: int
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"""
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if not (
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'_collocations' in self.__dict__
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and self._num == num
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and self._window_size == window_size
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):
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self._num = num
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self._window_size = window_size
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# print("Building collocations list")
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from nltk.corpus import stopwords
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ignored_words = stopwords.words('english')
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finder = BigramCollocationFinder.from_words(self.tokens, window_size)
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finder.apply_freq_filter(2)
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finder.apply_word_filter(lambda w: len(w) < 3 or w.lower() in ignored_words)
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bigram_measures = BigramAssocMeasures()
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self._collocations = finder.nbest(bigram_measures.likelihood_ratio, num)
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return [w1 + ' ' + w2 for w1, w2 in self._collocations]
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def collocations(self, num=20, window_size=2):
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"""
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Print collocations derived from the text, ignoring stopwords.
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:param num: The maximum number of collocations to print.
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:type num: int
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:param window_size: The number of tokens spanned by a collocation (default=2)
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:type window_size: int
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"""
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collocation_strings = [w1 + ' ' + w2 for w1, w2 in self.collocation_list(num, window_size)]
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print(tokenwrap(collocation_strings, separator="; "))
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def count(self, word):
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"""
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Count the number of times this word appears in the text.
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"""
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return self.tokens.count(word)
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def index(self, word):
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"""
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Find the index of the first occurrence of the word in the text.
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"""
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return self.tokens.index(word)
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def readability(self, method):
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# code from nltk_contrib.readability
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raise NotImplementedError
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def similar(self, word, num=20):
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"""
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Distributional similarity: find other words which appear in the
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same contexts as the specified word; list most similar words first.
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:param word: The word used to seed the similarity search
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:type word: str
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:param num: The number of words to generate (default=20)
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:type num: int
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:seealso: ContextIndex.similar_words()
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"""
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if '_word_context_index' not in self.__dict__:
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# print('Building word-context index...')
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self._word_context_index = ContextIndex(
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self.tokens, filter=lambda x: x.isalpha(), key=lambda s: s.lower()
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)
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# words = self._word_context_index.similar_words(word, num)
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word = word.lower()
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wci = self._word_context_index._word_to_contexts
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if word in wci.conditions():
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contexts = set(wci[word])
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fd = Counter(
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w
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for w in wci.conditions()
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for c in wci[w]
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if c in contexts and not w == word
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)
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words = [w for w, _ in fd.most_common(num)]
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print(tokenwrap(words))
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else:
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print("No matches")
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def common_contexts(self, words, num=20):
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"""
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Find contexts where the specified words appear; list
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most frequent common contexts first.
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:param words: The words used to seed the similarity search
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:type words: str
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:param num: The number of words to generate (default=20)
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:type num: int
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:seealso: ContextIndex.common_contexts()
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"""
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if '_word_context_index' not in self.__dict__:
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# print('Building word-context index...')
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self._word_context_index = ContextIndex(
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self.tokens, key=lambda s: s.lower()
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)
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try:
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fd = self._word_context_index.common_contexts(words, True)
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if not fd:
|
|
print("No common contexts were found")
|
|
else:
|
|
ranked_contexts = [w for w, _ in fd.most_common(num)]
|
|
print(tokenwrap(w1 + "_" + w2 for w1, w2 in ranked_contexts))
|
|
|
|
except ValueError as e:
|
|
print(e)
|
|
|
|
def dispersion_plot(self, words):
|
|
"""
|
|
Produce a plot showing the distribution of the words through the text.
|
|
Requires pylab to be installed.
|
|
|
|
:param words: The words to be plotted
|
|
:type words: list(str)
|
|
:seealso: nltk.draw.dispersion_plot()
|
|
"""
|
|
from nltk.draw import dispersion_plot
|
|
|
|
dispersion_plot(self, words)
|
|
|
|
def generate(self, words):
|
|
"""
|
|
Issues a reminder to users following the book online
|
|
"""
|
|
import warnings
|
|
|
|
warnings.warn(
|
|
'The generate() method is no longer available.', DeprecationWarning
|
|
)
|
|
|
|
def plot(self, *args):
|
|
"""
|
|
See documentation for FreqDist.plot()
|
|
:seealso: nltk.prob.FreqDist.plot()
|
|
"""
|
|
self.vocab().plot(*args)
|
|
|
|
def vocab(self):
|
|
"""
|
|
:seealso: nltk.prob.FreqDist
|
|
"""
|
|
if "_vocab" not in self.__dict__:
|
|
# print("Building vocabulary index...")
|
|
self._vocab = FreqDist(self)
|
|
return self._vocab
|
|
|
|
def findall(self, regexp):
|
|
"""
|
|
Find instances of the regular expression in the text.
|
|
The text is a list of tokens, and a regexp pattern to match
|
|
a single token must be surrounded by angle brackets. E.g.
|
|
|
|
>>> print('hack'); from nltk.book import text1, text5, text9
|
|
hack...
|
|
>>> text5.findall("<.*><.*><bro>")
|
|
you rule bro; telling you bro; u twizted bro
|
|
>>> text1.findall("<a>(<.*>)<man>")
|
|
monied; nervous; dangerous; white; white; white; pious; queer; good;
|
|
mature; white; Cape; great; wise; wise; butterless; white; fiendish;
|
|
pale; furious; better; certain; complete; dismasted; younger; brave;
|
|
brave; brave; brave
|
|
>>> text9.findall("<th.*>{3,}")
|
|
thread through those; the thought that; that the thing; the thing
|
|
that; that that thing; through these than through; them that the;
|
|
through the thick; them that they; thought that the
|
|
|
|
:param regexp: A regular expression
|
|
:type regexp: str
|
|
"""
|
|
|
|
if "_token_searcher" not in self.__dict__:
|
|
self._token_searcher = TokenSearcher(self)
|
|
|
|
hits = self._token_searcher.findall(regexp)
|
|
hits = [' '.join(h) for h in hits]
|
|
print(tokenwrap(hits, "; "))
|
|
|
|
# ////////////////////////////////////////////////////////////
|
|
# Helper Methods
|
|
# ////////////////////////////////////////////////////////////
|
|
|
|
_CONTEXT_RE = re.compile('\w+|[\.\!\?]')
|
|
|
|
def _context(self, tokens, i):
|
|
"""
|
|
One left & one right token, both case-normalized. Skip over
|
|
non-sentence-final punctuation. Used by the ``ContextIndex``
|
|
that is created for ``similar()`` and ``common_contexts()``.
|
|
"""
|
|
# Left context
|
|
j = i - 1
|
|
while j >= 0 and not self._CONTEXT_RE.match(tokens[j]):
|
|
j -= 1
|
|
left = tokens[j] if j != 0 else '*START*'
|
|
|
|
# Right context
|
|
j = i + 1
|
|
while j < len(tokens) and not self._CONTEXT_RE.match(tokens[j]):
|
|
j += 1
|
|
right = tokens[j] if j != len(tokens) else '*END*'
|
|
|
|
return (left, right)
|
|
|
|
# ////////////////////////////////////////////////////////////
|
|
# String Display
|
|
# ////////////////////////////////////////////////////////////
|
|
|
|
def __str__(self):
|
|
return '<Text: %s>' % self.name
|
|
|
|
def __repr__(self):
|
|
return '<Text: %s>' % self.name
|
|
|
|
|
|
# Prototype only; this approach will be slow to load
|
|
class TextCollection(Text):
|
|
"""A collection of texts, which can be loaded with list of texts, or
|
|
with a corpus consisting of one or more texts, and which supports
|
|
counting, concordancing, collocation discovery, etc. Initialize a
|
|
TextCollection as follows:
|
|
|
|
>>> import nltk.corpus
|
|
>>> from nltk.text import TextCollection
|
|
>>> print('hack'); from nltk.book import text1, text2, text3
|
|
hack...
|
|
>>> gutenberg = TextCollection(nltk.corpus.gutenberg)
|
|
>>> mytexts = TextCollection([text1, text2, text3])
|
|
|
|
Iterating over a TextCollection produces all the tokens of all the
|
|
texts in order.
|
|
"""
|
|
|
|
def __init__(self, source):
|
|
if hasattr(source, 'words'): # bridge to the text corpus reader
|
|
source = [source.words(f) for f in source.fileids()]
|
|
|
|
self._texts = source
|
|
Text.__init__(self, LazyConcatenation(source))
|
|
self._idf_cache = {}
|
|
|
|
def tf(self, term, text):
|
|
""" The frequency of the term in text. """
|
|
return text.count(term) / len(text)
|
|
|
|
def idf(self, term):
|
|
""" The number of texts in the corpus divided by the
|
|
number of texts that the term appears in.
|
|
If a term does not appear in the corpus, 0.0 is returned. """
|
|
# idf values are cached for performance.
|
|
idf = self._idf_cache.get(term)
|
|
if idf is None:
|
|
matches = len([True for text in self._texts if term in text])
|
|
if len(self._texts) == 0:
|
|
raise ValueError('IDF undefined for empty document collection')
|
|
idf = log(len(self._texts) / matches) if matches else 0.0
|
|
self._idf_cache[term] = idf
|
|
return idf
|
|
|
|
def tf_idf(self, term, text):
|
|
return self.tf(term, text) * self.idf(term)
|
|
|
|
|
|
def demo():
|
|
from nltk.corpus import brown
|
|
|
|
text = Text(brown.words(categories='news'))
|
|
print(text)
|
|
print()
|
|
print("Concordance:")
|
|
text.concordance('news')
|
|
print()
|
|
print("Distributionally similar words:")
|
|
text.similar('news')
|
|
print()
|
|
print("Collocations:")
|
|
text.collocations()
|
|
print()
|
|
# print("Automatically generated text:")
|
|
# text.generate()
|
|
# print()
|
|
print("Dispersion plot:")
|
|
text.dispersion_plot(['news', 'report', 'said', 'announced'])
|
|
print()
|
|
print("Vocabulary plot:")
|
|
text.plot(50)
|
|
print()
|
|
print("Indexing:")
|
|
print("text[3]:", text[3])
|
|
print("text[3:5]:", text[3:5])
|
|
print("text.vocab()['news']:", text.vocab()['news'])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
demo()
|
|
|
|
__all__ = [
|
|
"ContextIndex",
|
|
"ConcordanceIndex",
|
|
"TokenSearcher",
|
|
"Text",
|
|
"TextCollection",
|
|
]
|