# Natural Language Toolkit: TextTiling
#
# Copyright (C) 2001-2019 NLTK Project
# Author: George Boutsioukis
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
import re
import math
try:
import numpy
except ImportError:
pass
from nltk.tokenize.api import TokenizerI
BLOCK_COMPARISON, VOCABULARY_INTRODUCTION = 0, 1
LC, HC = 0, 1
DEFAULT_SMOOTHING = [0]
[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]
"""
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("[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(
"Similarity method {} not recognized".format(self.similarity_method)
)
if self.smoothing_method == DEFAULT_SMOOTHING:
smooth_scores = self._smooth_scores(gap_scores)
else:
raise ValueError(
"Smoothing method {} not recognized".format(self.smoothing_method)
)
# 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("\w+", text)
for match in matches:
wrdindex_list.append((match.group(), 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:
raise ValueError(
"No paragraph breaks were found(text too short perhaps?)"
)
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)
# SB: what is the purpose of this conditional?
if self.cutoff_policy == LC:
cutoff = avg - stdev / 2.0
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(object):
"""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']
[docs]class TokenSequence(object):
"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: http://www.scipy.org/Cookbook/SignalSmooth
[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 nltk.corpus import brown
from matplotlib import pylab
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()