Source code for nltk.tokenize.texttiling

# Natural Language Toolkit: TextTiling
# Copyright (C) 2001-2023 NLTK Project
# Author: George Boutsioukis
# URL: <>
# For license information, see LICENSE.TXT

import math
import re

    import numpy
except ImportError:

from nltk.tokenize.api import TokenizerI

LC, HC = 0, 1

[docs]class TextTilingTokenizer(TokenizerI): """Tokenize a document into topical sections using the TextTiling algorithm. This algorithm detects subtopic shifts based on the analysis of lexical co-occurrence patterns. The process starts by tokenizing the text into pseudosentences of a fixed size w. Then, depending on the method used, similarity scores are assigned at sentence gaps. The algorithm proceeds by detecting the peak differences between these scores and marking them as boundaries. The boundaries are normalized to the closest paragraph break and the segmented text is returned. :param w: Pseudosentence size :type w: int :param k: Size (in sentences) of the block used in the block comparison method :type k: int :param similarity_method: The method used for determining similarity scores: `BLOCK_COMPARISON` (default) or `VOCABULARY_INTRODUCTION`. :type similarity_method: constant :param stopwords: A list of stopwords that are filtered out (defaults to NLTK's stopwords corpus) :type stopwords: list(str) :param smoothing_method: The method used for smoothing the score plot: `DEFAULT_SMOOTHING` (default) :type smoothing_method: constant :param smoothing_width: The width of the window used by the smoothing method :type smoothing_width: int :param smoothing_rounds: The number of smoothing passes :type smoothing_rounds: int :param cutoff_policy: The policy used to determine the number of boundaries: `HC` (default) or `LC` :type cutoff_policy: constant >>> from nltk.corpus import brown >>> tt = TextTilingTokenizer(demo_mode=True) >>> text = brown.raw()[:4000] >>> s, ss, d, b = tt.tokenize(text) >>> b [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0] """
[docs] def __init__( self, w=20, k=10, similarity_method=BLOCK_COMPARISON, stopwords=None, smoothing_method=DEFAULT_SMOOTHING, smoothing_width=2, smoothing_rounds=1, cutoff_policy=HC, demo_mode=False, ): if stopwords is None: from nltk.corpus import stopwords stopwords = stopwords.words("english") self.__dict__.update(locals()) del self.__dict__["self"]
[docs] def tokenize(self, text): """Return a tokenized copy of *text*, where each "token" represents a separate topic.""" lowercase_text = text.lower() paragraph_breaks = self._mark_paragraph_breaks(text) text_length = len(lowercase_text) # Tokenization step starts here # Remove punctuation nopunct_text = "".join( c for c in lowercase_text if re.match(r"[a-z\-' \n\t]", c) ) nopunct_par_breaks = self._mark_paragraph_breaks(nopunct_text) tokseqs = self._divide_to_tokensequences(nopunct_text) # The morphological stemming step mentioned in the TextTile # paper is not implemented. A comment in the original C # implementation states that it offers no benefit to the # process. It might be interesting to test the existing # stemmers though. # words = _stem_words(words) # Filter stopwords for ts in tokseqs: ts.wrdindex_list = [ wi for wi in ts.wrdindex_list if wi[0] not in self.stopwords ] token_table = self._create_token_table(tokseqs, nopunct_par_breaks) # End of the Tokenization step # Lexical score determination if self.similarity_method == BLOCK_COMPARISON: gap_scores = self._block_comparison(tokseqs, token_table) elif self.similarity_method == VOCABULARY_INTRODUCTION: raise NotImplementedError("Vocabulary introduction not implemented") else: raise ValueError( f"Similarity method {self.similarity_method} not recognized" ) if self.smoothing_method == DEFAULT_SMOOTHING: smooth_scores = self._smooth_scores(gap_scores) else: raise ValueError(f"Smoothing method {self.smoothing_method} not recognized") # End of Lexical score Determination # Boundary identification depth_scores = self._depth_scores(smooth_scores) segment_boundaries = self._identify_boundaries(depth_scores) normalized_boundaries = self._normalize_boundaries( text, segment_boundaries, paragraph_breaks ) # End of Boundary Identification segmented_text = [] prevb = 0 for b in normalized_boundaries: if b == 0: continue segmented_text.append(text[prevb:b]) prevb = b if prevb < text_length: # append any text that may be remaining segmented_text.append(text[prevb:]) if not segmented_text: segmented_text = [text] if self.demo_mode: return gap_scores, smooth_scores, depth_scores, segment_boundaries return segmented_text
def _block_comparison(self, tokseqs, token_table): """Implements the block comparison method""" def blk_frq(tok, block): ts_occs = filter(lambda o: o[0] in block, token_table[tok].ts_occurences) freq = sum(tsocc[1] for tsocc in ts_occs) return freq gap_scores = [] numgaps = len(tokseqs) - 1 for curr_gap in range(numgaps): score_dividend, score_divisor_b1, score_divisor_b2 = 0.0, 0.0, 0.0 score = 0.0 # adjust window size for boundary conditions if curr_gap < self.k - 1: window_size = curr_gap + 1 elif curr_gap > numgaps - self.k: window_size = numgaps - curr_gap else: window_size = self.k b1 = [ts.index for ts in tokseqs[curr_gap - window_size + 1 : curr_gap + 1]] b2 = [ts.index for ts in tokseqs[curr_gap + 1 : curr_gap + window_size + 1]] for t in token_table: score_dividend += blk_frq(t, b1) * blk_frq(t, b2) score_divisor_b1 += blk_frq(t, b1) ** 2 score_divisor_b2 += blk_frq(t, b2) ** 2 try: score = score_dividend / math.sqrt(score_divisor_b1 * score_divisor_b2) except ZeroDivisionError: pass # score += 0.0 gap_scores.append(score) return gap_scores def _smooth_scores(self, gap_scores): "Wraps the smooth function from the SciPy Cookbook" return list( smooth(numpy.array(gap_scores[:]), window_len=self.smoothing_width + 1) ) def _mark_paragraph_breaks(self, text): """Identifies indented text or line breaks as the beginning of paragraphs""" MIN_PARAGRAPH = 100 pattern = re.compile("[ \t\r\f\v]*\n[ \t\r\f\v]*\n[ \t\r\f\v]*") matches = pattern.finditer(text) last_break = 0 pbreaks = [0] for pb in matches: if pb.start() - last_break < MIN_PARAGRAPH: continue else: pbreaks.append(pb.start()) last_break = pb.start() return pbreaks def _divide_to_tokensequences(self, text): "Divides the text into pseudosentences of fixed size" w = self.w wrdindex_list = [] matches = re.finditer(r"\w+", text) for match in matches: wrdindex_list.append((, match.start())) return [ TokenSequence(i / w, wrdindex_list[i : i + w]) for i in range(0, len(wrdindex_list), w) ] def _create_token_table(self, token_sequences, par_breaks): "Creates a table of TokenTableFields" token_table = {} current_par = 0 current_tok_seq = 0 pb_iter = par_breaks.__iter__() current_par_break = next(pb_iter) if current_par_break == 0: try: current_par_break = next(pb_iter) # skip break at 0 except StopIteration as e: raise ValueError( "No paragraph breaks were found(text too short perhaps?)" ) from e for ts in token_sequences: for word, index in ts.wrdindex_list: try: while index > current_par_break: current_par_break = next(pb_iter) current_par += 1 except StopIteration: # hit bottom pass if word in token_table: token_table[word].total_count += 1 if token_table[word].last_par != current_par: token_table[word].last_par = current_par token_table[word].par_count += 1 if token_table[word].last_tok_seq != current_tok_seq: token_table[word].last_tok_seq = current_tok_seq token_table[word].ts_occurences.append([current_tok_seq, 1]) else: token_table[word].ts_occurences[-1][1] += 1 else: # new word token_table[word] = TokenTableField( first_pos=index, ts_occurences=[[current_tok_seq, 1]], total_count=1, par_count=1, last_par=current_par, last_tok_seq=current_tok_seq, ) current_tok_seq += 1 return token_table def _identify_boundaries(self, depth_scores): """Identifies boundaries at the peaks of similarity score differences""" boundaries = [0 for x in depth_scores] avg = sum(depth_scores) / len(depth_scores) stdev = numpy.std(depth_scores) if self.cutoff_policy == LC: cutoff = avg - stdev else: cutoff = avg - stdev / 2.0 depth_tuples = sorted(zip(depth_scores, range(len(depth_scores)))) depth_tuples.reverse() hp = list(filter(lambda x: x[0] > cutoff, depth_tuples)) for dt in hp: boundaries[dt[1]] = 1 for dt2 in hp: # undo if there is a boundary close already if ( dt[1] != dt2[1] and abs(dt2[1] - dt[1]) < 4 and boundaries[dt2[1]] == 1 ): boundaries[dt[1]] = 0 return boundaries def _depth_scores(self, scores): """Calculates the depth of each gap, i.e. the average difference between the left and right peaks and the gap's score""" depth_scores = [0 for x in scores] # clip boundaries: this holds on the rule of thumb(my thumb) # that a section shouldn't be smaller than at least 2 # pseudosentences for small texts and around 5 for larger ones. clip = min(max(len(scores) // 10, 2), 5) index = clip for gapscore in scores[clip:-clip]: lpeak = gapscore for score in scores[index::-1]: if score >= lpeak: lpeak = score else: break rpeak = gapscore for score in scores[index:]: if score >= rpeak: rpeak = score else: break depth_scores[index] = lpeak + rpeak - 2 * gapscore index += 1 return depth_scores def _normalize_boundaries(self, text, boundaries, paragraph_breaks): """Normalize the boundaries identified to the original text's paragraph breaks""" norm_boundaries = [] char_count, word_count, gaps_seen = 0, 0, 0 seen_word = False for char in text: char_count += 1 if char in " \t\n" and seen_word: seen_word = False word_count += 1 if char not in " \t\n" and not seen_word: seen_word = True if gaps_seen < len(boundaries) and word_count > ( max(gaps_seen * self.w, self.w) ): if boundaries[gaps_seen] == 1: # find closest paragraph break best_fit = len(text) for br in paragraph_breaks: if best_fit > abs(br - char_count): best_fit = abs(br - char_count) bestbr = br else: break if bestbr not in norm_boundaries: # avoid duplicates norm_boundaries.append(bestbr) gaps_seen += 1 return norm_boundaries
[docs]class TokenTableField: """A field in the token table holding parameters for each token, used later in the process"""
[docs] 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"]
[docs]class TokenSequence: "A token list with its original length and its index"
[docs] 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:
[docs]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]
[docs]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()