Source code for nltk.tokenize.texttiling

http://nltk.org/_modules/nltk/tokenize/texttiling.html

# Natural Language Toolkit: TextTiling## Copyright (C) 2001-2012 NLTK Project# Author: George Boutsioukis## URL: <http://www.nltk.org/># For license information, see LICENSE.TXTimport reimport mathtry:    import numpyexcept ImportError:    passfrom nltk.tokenize.api import TokenizerIBLOCK_COMPARISON, VOCABULARY_INTRODUCTION = range(2)LC, HC = range(2)DEFAULT_SMOOTHING = range(1)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    """    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']    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 = filter(lambda wi: wi[0] not in self.stopwords,                                      ts.wrdindex_list)        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")        if self.smoothing_method == DEFAULT_SMOOTHING:            smooth_scores = self._smooth_scores(gap_scores)        # 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 = pb_iter.next()        if current_par_break == 0:            try:                current_par_break = pb_iter.next() #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 = pb_iter.next()                        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)        numpy.stdev = numpy.std(depth_scores)        #SB: what is the purpose of this conditional?        if self.cutoff_policy == LC:            cutoff = avg-numpy.stdev/2.0        else:            cutoff = avg-numpy.stdev/2.0        depth_tuples = zip(depth_scores, range(len(depth_scores)))        depth_tuples.sort()        depth_tuples.reverse()        hp = 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        # SB: next three lines are redundant as depth_scores is already full of zeros        for i in range(clip):            depth_scores[i] = 0            depth_scores[-i-1] = 0        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_boundariesclass 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']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/SignalSmoothdef 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 not window 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 nltk.corpus import brown    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()if __name__ == "__main__":    import doctest    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)