476 lines
16 KiB
Python
476 lines
16 KiB
Python
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# Natural Language Toolkit: TextTiling
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#
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# Copyright (C) 2001-2023 NLTK Project
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# Author: George Boutsioukis
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#
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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import math
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import re
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try:
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import numpy
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except ImportError:
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pass
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from nltk.tokenize.api import TokenizerI
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BLOCK_COMPARISON, VOCABULARY_INTRODUCTION = 0, 1
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LC, HC = 0, 1
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DEFAULT_SMOOTHING = [0]
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class TextTilingTokenizer(TokenizerI):
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"""Tokenize a document into topical sections using the TextTiling algorithm.
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This algorithm detects subtopic shifts based on the analysis of lexical
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co-occurrence patterns.
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The process starts by tokenizing the text into pseudosentences of
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a fixed size w. Then, depending on the method used, similarity
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scores are assigned at sentence gaps. The algorithm proceeds by
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detecting the peak differences between these scores and marking
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them as boundaries. The boundaries are normalized to the closest
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paragraph break and the segmented text is returned.
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:param w: Pseudosentence size
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:type w: int
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:param k: Size (in sentences) of the block used in the block comparison method
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:type k: int
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:param similarity_method: The method used for determining similarity scores:
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`BLOCK_COMPARISON` (default) or `VOCABULARY_INTRODUCTION`.
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:type similarity_method: constant
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:param stopwords: A list of stopwords that are filtered out (defaults to NLTK's stopwords corpus)
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:type stopwords: list(str)
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:param smoothing_method: The method used for smoothing the score plot:
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`DEFAULT_SMOOTHING` (default)
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:type smoothing_method: constant
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:param smoothing_width: The width of the window used by the smoothing method
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:type smoothing_width: int
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:param smoothing_rounds: The number of smoothing passes
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:type smoothing_rounds: int
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:param cutoff_policy: The policy used to determine the number of boundaries:
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`HC` (default) or `LC`
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:type cutoff_policy: constant
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>>> from nltk.corpus import brown
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>>> tt = TextTilingTokenizer(demo_mode=True)
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>>> text = brown.raw()[:4000]
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>>> s, ss, d, b = tt.tokenize(text)
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>>> b
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[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]
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"""
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def __init__(
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self,
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w=20,
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k=10,
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similarity_method=BLOCK_COMPARISON,
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stopwords=None,
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smoothing_method=DEFAULT_SMOOTHING,
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smoothing_width=2,
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smoothing_rounds=1,
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cutoff_policy=HC,
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demo_mode=False,
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):
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if stopwords is None:
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from nltk.corpus import stopwords
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stopwords = stopwords.words("english")
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self.__dict__.update(locals())
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del self.__dict__["self"]
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def tokenize(self, text):
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"""Return a tokenized copy of *text*, where each "token" represents
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a separate topic."""
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lowercase_text = text.lower()
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paragraph_breaks = self._mark_paragraph_breaks(text)
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text_length = len(lowercase_text)
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# Tokenization step starts here
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# Remove punctuation
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nopunct_text = "".join(
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c for c in lowercase_text if re.match(r"[a-z\-' \n\t]", c)
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)
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nopunct_par_breaks = self._mark_paragraph_breaks(nopunct_text)
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tokseqs = self._divide_to_tokensequences(nopunct_text)
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# The morphological stemming step mentioned in the TextTile
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# paper is not implemented. A comment in the original C
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# implementation states that it offers no benefit to the
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# process. It might be interesting to test the existing
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# stemmers though.
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# words = _stem_words(words)
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# Filter stopwords
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for ts in tokseqs:
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ts.wrdindex_list = [
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wi for wi in ts.wrdindex_list if wi[0] not in self.stopwords
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]
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token_table = self._create_token_table(tokseqs, nopunct_par_breaks)
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# End of the Tokenization step
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# Lexical score determination
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if self.similarity_method == BLOCK_COMPARISON:
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gap_scores = self._block_comparison(tokseqs, token_table)
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elif self.similarity_method == VOCABULARY_INTRODUCTION:
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raise NotImplementedError("Vocabulary introduction not implemented")
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else:
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raise ValueError(
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f"Similarity method {self.similarity_method} not recognized"
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)
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if self.smoothing_method == DEFAULT_SMOOTHING:
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smooth_scores = self._smooth_scores(gap_scores)
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else:
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raise ValueError(f"Smoothing method {self.smoothing_method} not recognized")
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# End of Lexical score Determination
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# Boundary identification
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depth_scores = self._depth_scores(smooth_scores)
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segment_boundaries = self._identify_boundaries(depth_scores)
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normalized_boundaries = self._normalize_boundaries(
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text, segment_boundaries, paragraph_breaks
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)
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# End of Boundary Identification
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segmented_text = []
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prevb = 0
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for b in normalized_boundaries:
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if b == 0:
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continue
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segmented_text.append(text[prevb:b])
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prevb = b
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if prevb < text_length: # append any text that may be remaining
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segmented_text.append(text[prevb:])
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if not segmented_text:
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segmented_text = [text]
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if self.demo_mode:
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return gap_scores, smooth_scores, depth_scores, segment_boundaries
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return segmented_text
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def _block_comparison(self, tokseqs, token_table):
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"""Implements the block comparison method"""
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def blk_frq(tok, block):
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ts_occs = filter(lambda o: o[0] in block, token_table[tok].ts_occurences)
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freq = sum(tsocc[1] for tsocc in ts_occs)
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return freq
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gap_scores = []
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numgaps = len(tokseqs) - 1
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for curr_gap in range(numgaps):
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score_dividend, score_divisor_b1, score_divisor_b2 = 0.0, 0.0, 0.0
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score = 0.0
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# adjust window size for boundary conditions
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if curr_gap < self.k - 1:
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window_size = curr_gap + 1
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elif curr_gap > numgaps - self.k:
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window_size = numgaps - curr_gap
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else:
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window_size = self.k
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b1 = [ts.index for ts in tokseqs[curr_gap - window_size + 1 : curr_gap + 1]]
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b2 = [ts.index for ts in tokseqs[curr_gap + 1 : curr_gap + window_size + 1]]
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for t in token_table:
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score_dividend += blk_frq(t, b1) * blk_frq(t, b2)
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score_divisor_b1 += blk_frq(t, b1) ** 2
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score_divisor_b2 += blk_frq(t, b2) ** 2
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try:
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score = score_dividend / math.sqrt(score_divisor_b1 * score_divisor_b2)
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except ZeroDivisionError:
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pass # score += 0.0
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gap_scores.append(score)
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return gap_scores
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def _smooth_scores(self, gap_scores):
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"Wraps the smooth function from the SciPy Cookbook"
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return list(
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smooth(numpy.array(gap_scores[:]), window_len=self.smoothing_width + 1)
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)
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def _mark_paragraph_breaks(self, text):
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"""Identifies indented text or line breaks as the beginning of
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paragraphs"""
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MIN_PARAGRAPH = 100
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pattern = re.compile("[ \t\r\f\v]*\n[ \t\r\f\v]*\n[ \t\r\f\v]*")
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matches = pattern.finditer(text)
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last_break = 0
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pbreaks = [0]
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for pb in matches:
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if pb.start() - last_break < MIN_PARAGRAPH:
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continue
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else:
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pbreaks.append(pb.start())
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last_break = pb.start()
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return pbreaks
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def _divide_to_tokensequences(self, text):
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"Divides the text into pseudosentences of fixed size"
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w = self.w
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wrdindex_list = []
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matches = re.finditer(r"\w+", text)
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for match in matches:
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wrdindex_list.append((match.group(), match.start()))
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return [
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TokenSequence(i / w, wrdindex_list[i : i + w])
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for i in range(0, len(wrdindex_list), w)
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]
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def _create_token_table(self, token_sequences, par_breaks):
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"Creates a table of TokenTableFields"
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token_table = {}
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current_par = 0
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current_tok_seq = 0
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pb_iter = par_breaks.__iter__()
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current_par_break = next(pb_iter)
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if current_par_break == 0:
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try:
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current_par_break = next(pb_iter) # skip break at 0
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except StopIteration as e:
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raise ValueError(
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"No paragraph breaks were found(text too short perhaps?)"
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) from e
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for ts in token_sequences:
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for word, index in ts.wrdindex_list:
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try:
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while index > current_par_break:
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current_par_break = next(pb_iter)
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current_par += 1
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except StopIteration:
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# hit bottom
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pass
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if word in token_table:
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token_table[word].total_count += 1
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if token_table[word].last_par != current_par:
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token_table[word].last_par = current_par
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token_table[word].par_count += 1
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if token_table[word].last_tok_seq != current_tok_seq:
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token_table[word].last_tok_seq = current_tok_seq
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token_table[word].ts_occurences.append([current_tok_seq, 1])
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else:
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token_table[word].ts_occurences[-1][1] += 1
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else: # new word
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token_table[word] = TokenTableField(
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first_pos=index,
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ts_occurences=[[current_tok_seq, 1]],
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total_count=1,
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par_count=1,
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last_par=current_par,
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last_tok_seq=current_tok_seq,
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)
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current_tok_seq += 1
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return token_table
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def _identify_boundaries(self, depth_scores):
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"""Identifies boundaries at the peaks of similarity score
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differences"""
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boundaries = [0 for x in depth_scores]
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avg = sum(depth_scores) / len(depth_scores)
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stdev = numpy.std(depth_scores)
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if self.cutoff_policy == LC:
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cutoff = avg - stdev
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else:
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cutoff = avg - stdev / 2.0
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depth_tuples = sorted(zip(depth_scores, range(len(depth_scores))))
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depth_tuples.reverse()
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hp = list(filter(lambda x: x[0] > cutoff, depth_tuples))
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for dt in hp:
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boundaries[dt[1]] = 1
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for dt2 in hp: # undo if there is a boundary close already
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if (
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dt[1] != dt2[1]
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and abs(dt2[1] - dt[1]) < 4
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and boundaries[dt2[1]] == 1
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):
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boundaries[dt[1]] = 0
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return boundaries
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def _depth_scores(self, scores):
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"""Calculates the depth of each gap, i.e. the average difference
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between the left and right peaks and the gap's score"""
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depth_scores = [0 for x in scores]
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# clip boundaries: this holds on the rule of thumb(my thumb)
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# that a section shouldn't be smaller than at least 2
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# pseudosentences for small texts and around 5 for larger ones.
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clip = min(max(len(scores) // 10, 2), 5)
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index = clip
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for gapscore in scores[clip:-clip]:
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lpeak = gapscore
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for score in scores[index::-1]:
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if score >= lpeak:
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lpeak = score
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else:
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break
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rpeak = gapscore
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for score in scores[index:]:
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if score >= rpeak:
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rpeak = score
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else:
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break
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depth_scores[index] = lpeak + rpeak - 2 * gapscore
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index += 1
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return depth_scores
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def _normalize_boundaries(self, text, boundaries, paragraph_breaks):
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"""Normalize the boundaries identified to the original text's
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paragraph breaks"""
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norm_boundaries = []
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char_count, word_count, gaps_seen = 0, 0, 0
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seen_word = False
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for char in text:
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char_count += 1
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if char in " \t\n" and seen_word:
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seen_word = False
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word_count += 1
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if char not in " \t\n" and not seen_word:
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seen_word = True
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if gaps_seen < len(boundaries) and word_count > (
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max(gaps_seen * self.w, self.w)
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):
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if boundaries[gaps_seen] == 1:
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# find closest paragraph break
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best_fit = len(text)
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for br in paragraph_breaks:
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if best_fit > abs(br - char_count):
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best_fit = abs(br - char_count)
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bestbr = br
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else:
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break
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if bestbr not in norm_boundaries: # avoid duplicates
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norm_boundaries.append(bestbr)
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gaps_seen += 1
|
||
|
|
|
||
|
|
return norm_boundaries
|
||
|
|
|
||
|
|
|
||
|
|
class TokenTableField:
|
||
|
|
"""A field in the token table holding parameters for each token,
|
||
|
|
used later in the process"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
first_pos,
|
||
|
|
ts_occurences,
|
||
|
|
total_count=1,
|
||
|
|
par_count=1,
|
||
|
|
last_par=0,
|
||
|
|
last_tok_seq=None,
|
||
|
|
):
|
||
|
|
self.__dict__.update(locals())
|
||
|
|
del self.__dict__["self"]
|
||
|
|
|
||
|
|
|
||
|
|
class TokenSequence:
|
||
|
|
"A token list with its original length and its index"
|
||
|
|
|
||
|
|
def __init__(self, index, wrdindex_list, original_length=None):
|
||
|
|
original_length = original_length or len(wrdindex_list)
|
||
|
|
self.__dict__.update(locals())
|
||
|
|
del self.__dict__["self"]
|
||
|
|
|
||
|
|
|
||
|
|
# Pasted from the SciPy cookbook: https://www.scipy.org/Cookbook/SignalSmooth
|
||
|
|
def smooth(x, window_len=11, window="flat"):
|
||
|
|
"""smooth the data using a window with requested size.
|
||
|
|
|
||
|
|
This method is based on the convolution of a scaled window with the signal.
|
||
|
|
The signal is prepared by introducing reflected copies of the signal
|
||
|
|
(with the window size) in both ends so that transient parts are minimized
|
||
|
|
in the beginning and end part of the output signal.
|
||
|
|
|
||
|
|
:param x: the input signal
|
||
|
|
:param window_len: the dimension of the smoothing window; should be an odd integer
|
||
|
|
:param window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
|
||
|
|
flat window will produce a moving average smoothing.
|
||
|
|
|
||
|
|
:return: the smoothed signal
|
||
|
|
|
||
|
|
example::
|
||
|
|
|
||
|
|
t=linspace(-2,2,0.1)
|
||
|
|
x=sin(t)+randn(len(t))*0.1
|
||
|
|
y=smooth(x)
|
||
|
|
|
||
|
|
:see also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve,
|
||
|
|
scipy.signal.lfilter
|
||
|
|
|
||
|
|
TODO: the window parameter could be the window itself if an array instead of a string
|
||
|
|
"""
|
||
|
|
|
||
|
|
if x.ndim != 1:
|
||
|
|
raise ValueError("smooth only accepts 1 dimension arrays.")
|
||
|
|
|
||
|
|
if x.size < window_len:
|
||
|
|
raise ValueError("Input vector needs to be bigger than window size.")
|
||
|
|
|
||
|
|
if window_len < 3:
|
||
|
|
return x
|
||
|
|
|
||
|
|
if window not in ["flat", "hanning", "hamming", "bartlett", "blackman"]:
|
||
|
|
raise ValueError(
|
||
|
|
"Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
|
||
|
|
)
|
||
|
|
|
||
|
|
s = numpy.r_[2 * x[0] - x[window_len:1:-1], x, 2 * x[-1] - x[-1:-window_len:-1]]
|
||
|
|
|
||
|
|
# print(len(s))
|
||
|
|
if window == "flat": # moving average
|
||
|
|
w = numpy.ones(window_len, "d")
|
||
|
|
else:
|
||
|
|
w = eval("numpy." + window + "(window_len)")
|
||
|
|
|
||
|
|
y = numpy.convolve(w / w.sum(), s, mode="same")
|
||
|
|
|
||
|
|
return y[window_len - 1 : -window_len + 1]
|
||
|
|
|
||
|
|
|
||
|
|
def demo(text=None):
|
||
|
|
from matplotlib import pylab
|
||
|
|
|
||
|
|
from nltk.corpus import brown
|
||
|
|
|
||
|
|
tt = TextTilingTokenizer(demo_mode=True)
|
||
|
|
if text is None:
|
||
|
|
text = brown.raw()[:10000]
|
||
|
|
s, ss, d, b = tt.tokenize(text)
|
||
|
|
pylab.xlabel("Sentence Gap index")
|
||
|
|
pylab.ylabel("Gap Scores")
|
||
|
|
pylab.plot(range(len(s)), s, label="Gap Scores")
|
||
|
|
pylab.plot(range(len(ss)), ss, label="Smoothed Gap scores")
|
||
|
|
pylab.plot(range(len(d)), d, label="Depth scores")
|
||
|
|
pylab.stem(range(len(b)), b)
|
||
|
|
pylab.legend()
|
||
|
|
pylab.show()
|