A First Exercise in Natural Language Processing with Python: Counting Hapaxes

A first exercise

Counting hapaxes (words which occur only once in a text or corpus) is an easy enough problem that makes use of both simple data structures and some fundamental tasks of natural language processing (NLP): tokenization (dividing a text into words), stemming, and part-of-speech tagging for lemmatization. For that reason it makes a good exercise to get started with NLP in a new language or library.

As a first exercise in implementing NLP tasks with Python, then, we’ll write a script which outputs the count and a list of the hapaxes in the following paragraph (our script can also be run on an arbitrary input file). You can follow along, or try it yourself and then compare your solution to mine.

Cory Linguist, a cautious corpus linguist, in creating a corpus of courtship correspondence, corrupted a crucial link. Now, if Cory Linguist, a careful corpus linguist, in creating a corpus of courtship correspondence, corrupted a crucial link, see that YOU, in creating a corpus of courtship correspondence, corrupt not a crucial link.

To keep things simple, ignore punctuation and case. To make things complex, count hapaxes in all three of word form, stemmed form, and lemma form. The final program (hapaxes.py) is listed at the end of this post. The sections below walk through it in detail for the beginning NLP/Python programmer.

Natural language processing with Python

There are several NLP packages available to the Python programmer. The most well-known is the Natural Language Toolkit (NLTK), which is the subject of the popular book Natural Language Processing with Python by Bird et al. NLTK has a focus on education/research with a rather sprawling API. Pattern is a Python package for datamining the WWW which includes submodules for language processing and machine learning. Polyglot is a language library focusing on “massive multilingual applications.” Many of its features support over 100 languages (but it doesn’t seem to have a stemmer or lemmatizer builtin). And there is Matthew Honnibal’s spaCy, an “industrial strength” NLP library focused on performance and integration with machine learning models.

If you don’t already know which library you want to use, I recommend starting with NLTK because there are so many online resources available for it. The program presented below actually presents five solutions to counting hapaxes, which will hopefully give you a feel for a few of the libraries mentioned above:

  • Word forms - counts unique spellings (normalized for case). This uses plain Python (no NLP packages required)

  • NLTK stems - counts unique stems using a stemmer provided by NLTK

  • NLTK lemmas - counts unique lemma forms using NLTK’s part of speech tagger and interface to the WordNet lemmatizer

  • spaCy lemmas - counts unique lemma forms using the spaCy NLP package

Installation

This tutorial assumes you already have Python installed on your system and have some experience using the interpreter. I recommend referring to each package’s project page for installation instructions, but here is one way using pip. As explained below, each of the NLP packages are optional; feel free to install only the ones you’re interested in playing with.

# Install NLTK:
$ pip install nltk

# Launch the NLTK downloader
# find and download the 'wordnet' and
# 'averaged_perceptron_tagger' packages:
$ python -c 'import nltk; nltk.download()'

# install spaCy:
$ pip install spacy

# install spaCy en model:
$ python -m spacy download en_core_web_sm

Optional dependency on Python modules

It would be nice if our script didn’t depend on any particular NLP package so that it could still run even if one or more of them were not installed (using only the functionality provided by whichever packages are installed).

One way to implement a script with optional package dependencies in Python is to try to import a module, and if we get an ImportError exception we mark the package as uninstalled (by setting a variable with the module’s name to None) which we can check for later in our code:

[hapaxes.py: 63-98]
### Imports
#
# Import some Python 3 features to use in Python 2
from __future__ import print_function
from __future__ import unicode_literals

# gives us access to command-line arguments
import sys

# The Counter collection is a convenient layer on top of
# python's standard dictionary type for counting iterables.
from collections import Counter

# The standard python regular expression module:
import re

try:
    # Import NLTK if it is installed
    import nltk

    # This imports NLTK's implementation of the Snowball
    # stemmer algorithm
    from nltk.stem.snowball import SnowballStemmer

    # NLTK's interface to the WordNet lemmatizer
    from nltk.stem.wordnet import WordNetLemmatizer
except ImportError:
    nltk = None
    print("NLTK is not installed, so we won't use it.")

try:
    # Import spaCy if it is installed
    import spacy
except ImportError:
    spacy = None
    print("spaCy is not installed, so we won't use it.")

Tokenization

Tokenization is the process of splitting a string into lexical ‘tokens’ — usually words or sentences. In languages with space-separated words, satisfactory tokenization can often be accomplished with a few simple rules, though ambiguous punctuation can cause errors (such as mistaking a period after an abbreviation as the end of a sentence). Some tokenizers use statistical inference (trained on a corpus with known token boundaries) to recognize tokens.

In our case we need to break the text into a list of words in order to find the hapaxes. But since we are not interested in punctuation or capitalization, we can make tokenization very simple by first normalizing the text to lower case and stripping out every punctuation symbol:

[hapaxes.py: 100-119]
def normalize_tokenize(string):
    """
    Takes a string, normalizes it (makes it lowercase and
    removes punctuation), and then splits it into a list of
    words.

    Note that everything in this function is plain Python
    without using NLTK (although as noted below, NLTK provides
    some more sophisticated tokenizers we could have used).
    """
    # make lowercase
    norm = string.lower()

    # remove punctuation
    norm = re.sub(r'(?u)[^\w\s]', '', norm) (1)

    # split into words
    tokens = norm.split()

    return tokens
1 Remove punctuation by replacing everything that is not a word (\w) or whitespace (\s) with an empty string. The (?u) flag at the beginning of the regex enables unicode matching for the \w and \s character classes in Python 2 (unicode is the default with Python 3).

Our tokenizer produces output like this:

>>> normalize_tokenize("This is a test sentence of white-space separated words.")
['this', 'is', 'a', 'test', 'sentence', 'of', 'whitespace', 'separated', 'words']

Instead of simply removing punctuation and then splitting words on whitespace, we could have used one of the tokenizers provided by NLTK. Specifically the word_tokenize() method, which first splits the text into sentences using a pre-trained English sentences tokenizer (sent_tokenize), and then finds words using regular expressions in the style of the Penn Treebank tokens.

# We could have done it this way (requires the
# 'punkt' data package):
from nltk.tokenize import word_tokenize
tokens = word_tokenize(norm)

The main advantage of word_tokenize() is that it will turn contractions into separate tokens. But using Python’s standard split() is good enough for our purposes.

Counting word forms

We can use the tokenizer defined above to get a list of words from any string, so now we need a way to count how many times each word occurs. Those that occur only once are our word-form hapaxes.

[hapaxes.py: 121-135]
def word_form_hapaxes(tokens):
    """
    Takes a list of tokens and returns a list of the
    wordform hapaxes (those wordforms that only appear once)

    For wordforms this is simple enough to do in plain
    Python without an NLP package, especially using the Counter
    type from the collections module (part of the Python
    standard library).
    """

    counts = Counter(tokens) (1)
    hapaxes = [word for word in counts if counts[word] == 1] (2)

    return hapaxes
1 Use the convenient Counter class from Python’s standard library to count the occurrences of each token. Counter is a subclass of the standard dict type; its constructor takes a list of items from which it builds a dictionary whose keys are elements from the list and whose values are the number of times each element appeared in the list.
2 This list comprehension creates a list from the Counter dictionary containing only the dictionary keys that have a count of 1. These are our hapaxes.

Stemming and Lemmatization

If we use our two functions to first tokenize and then find the hapaxes in our example text, we get this output:

>>> text = "Cory Linguist, a cautious corpus linguist, in creating a corpus of courtship correspondence, corrupted a crucial link. Now, if Cory Linguist, a careful corpus linguist, in creating a corpus of courtship correspondence, corrupted a crucial link, see that YOU, in creating a corpus of courtship correspondence, corrupt not a crucial link."
>>> tokens = normalize_tokenize(text)
>>> word_form_hapaxes(tokens)
['now', 'not', 'that', 'see', 'if', 'corrupt', 'you', 'careful', 'cautious']

Notice that ‘corrupt’ is counted as a hapax even though the text also includes two instances of the word ‘corrupted’. That is expected because ‘corrupt’ and ‘corrupted’ are different word-forms, but if we want to count word roots regardless of their inflections we must process our tokens further. There are two main methods we can try:

  • Stemming uses an algorithm (and/or a lookup table) to remove the suffix of tokens so that words with the same base but different inflections are reduced to the same form. For example: ‘argued’ and ‘arguing’ are both stemmed to ‘argu’.

  • Lemmatization reduces tokens to their lemmas, their canonical dictionary form. For example, ‘argued’ and ‘arguing’ are both lemmatized to ‘argue’.

Stemming with NLTK

In 1980 Martin Porter published a stemming algorithm which has become a standard way to stem English words. His algorithm was implemented so many times, and with so many errors, that he later created a programming language called Snowball to help clearly and exactly define stemmers. NLTK includes a Python port of the Snowball implementation of an improved version of Porter’s original stemmer:

[hapaxes.py: 137-153]
def nltk_stem_hapaxes(tokens):
    """
    Takes a list of tokens and returns a list of the word
    stem hapaxes.
    """
    if not nltk: (1)
        # Only run if NLTK is loaded
        return None

    # Apply NLTK's Snowball stemmer algorithm to tokens:
    stemmer = SnowballStemmer("english")
    stems = [stemmer.stem(token) for token in tokens]

    # Filter down to hapaxes:
    counts = nltk.FreqDist(stems) (2)
    hapaxes = counts.hapaxes() (3)
    return hapaxes
1 Here we check if the nltk module was loaded; if it was not (presumably because it is not installed), we return without trying to run the stemmer.
2 NLTK’s FreqDist class subclasses the Counter container type we used above to count word-forms. It adds some methods useful for calculating frequency distributions.
3 The FreqDist class also adds a hapaxes() method, which is implemented exactly like the list comprehension we used to count word-form hapaxes.

Running nltk_stem_hapaxes() on our tokenized example text produces this list of stem hapaxes:

>>> nltk_stem_hapaxes(tokens)
['now', 'cautious', 'that', 'not', 'see', 'you', 'care', 'if']

Notice that ‘corrupt’ is no longer counted as a hapax (since it shares a stem with ‘corrupted’), and ‘careful’ has been stemmed to ‘care’.

Lemmatization with NLTK

NLTK provides a lemmatizer (the WordNetLemmatizer class in nltk.stem.wordnet) which tries to find a word’s lemma form with help from the WordNet corpus (which can be downloaded by running nltk.download() from an interactive python prompt — refer to “Installing NLTK Data” for general instructions).

In order to resolve ambiguous cases, lemmatization usually requires tokens to be accompanied by part-of-speech tags. For example, the word lemma for rose depends on whether it is used as a noun or a verb:

>>> lemmer = WordNetLemmatizer()
>>> lemmer.lemmatize('rose', 'n') # tag as noun
'rose'
>>> lemmer.lemmatize('rose', 'v') # tag as verb
'rise'

Since we are operating on untagged tokens, we’ll first run them through an automated part-of-speech tagger provided by NLTK (it uses a pre-trained perceptron tagger originally by Matthew Honnibal: “A Good Part-of-Speech Tagger in about 200 Lines of Python”). The tagger requires the training data available in the 'averaged_perceptron_tagger.pickle' file which can be downloaded by running nltk.download() from an interactive python prompt.

[hapaxes.py: 155-176]
def nltk_lemma_hapaxes(tokens):
    """
    Takes a list of tokens and returns a list of the lemma
    hapaxes.
    """
    if not nltk:
        # Only run if NLTK is loaded
        return None

    # Tag tokens with part-of-speech:
    tagged = nltk.pos_tag(tokens) (1)

    # Convert our Treebank-style tags to WordNet-style tags.
    tagged = [(word, pt_to_wn(tag))
                     for (word, tag) in tagged] (2)

    # Lemmatize:
    lemmer = WordNetLemmatizer()
    lemmas = [lemmer.lemmatize(token, pos)
                     for (token, pos) in tagged] (3)

    return nltk_stem_hapaxes(lemmas) (4)
1 This turns our list of tokens into a list of 2-tuples: [(token1, tag1), (token2, tag2)…​]
2 We must convert between the tags returned by pos_tag() and the tags expected by the WordNet lemmatizer. This is done by applying the pt_to_wn() function (defined below) to each tag.
3 Pass each token and POS tag to the WordNet lemmatizer.
4 If a lemma is not found for a token, then it is returned from lemmatize() unchanged. To ensure these unhandled words don’t contribute spurious hapaxes, we pass our lemmatized tokens through the word stemmer for good measure (which also filters the list down to only hapaxes).

As noted above, the tags returned by pos_tag() are Penn Treebank style tags while the WordNet lemmatizer uses its own tag set (defined in the nltk.corpus.reader.wordnet module, though that is not very clear from the NLTK documentation). The pt_to_wn() function converts Treebank tags to the tags required for lemmatization:

[hapaxes.py: 178-209]
def pt_to_wn(pos):
    """
    Takes a Penn Treebank tag and converts it to an
    appropriate WordNet equivalent for lemmatization.

    A list of Penn Treebank tags is available at:
    https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html
    """

    from nltk.corpus.reader.wordnet import NOUN, VERB, ADJ, ADV

    pos = pos.lower()

    if pos.startswith('jj'):
        tag = ADJ
    elif pos == 'md':
        # Modal auxiliary verbs
        tag = VERB
    elif pos.startswith('rb'):
        tag = ADV
    elif pos.startswith('vb'):
        tag = VERB
    elif pos == 'wrb':
        # Wh-adverb (how, however, whence, whenever...)
        tag = ADV
    else:
        # default to NOUN
        # This is not strictly correct, but it is good
        # enough for lemmatization.
        tag = NOUN

    return tag

Finding hapaxes with spaCy

Unlike the NLTK API, spaCy is designed to tokenize, parse, and tag a text all by calling the single function returned by spacy.load(). The spaCy parser returns a ‘document’ object which contains all the tokens, their lemmas, etc. According to the spaCy documentation, “Lemmatization is performed using the WordNet data, but extended to also cover closed-class words such as pronouns.” The function below shows how to find the lemma hapaxes in a spaCy document.

spaCy’s models load quite a bit of data from disk which can cause script startup to be slow making it more suitable for long-running programs than for one-off scripts like ours.
[hapaxes.py: 211-234]
def spacy_hapaxes(rawtext):
    """
    Takes plain text and returns a list of lemma hapaxes using
    the spaCy NLP package.
    """
    if not spacy:
        # Only run if spaCy is installed
        return None

    # Load the English spaCy parser
    spacy_parse = spacy.load('en_core_web_sm')

    # Tokenize, parse, and tag text:
    doc = spacy_parse(rawtext)

    lemmas = [token.lemma_ for token in doc
            if not token.is_punct and not token.is_space] (1)

    # Now we can get a count of every lemma:
    counts = Counter(lemmas) (2)

    # We are interested in lemmas which appear only once
    hapaxes = [lemma for lemma in counts if counts[lemma] == 1]
    return hapaxes
1 This list comprehension collects the lemma form (token.lemma_ of all tokens in the spaCy document which are not punctuation (token.is_punct) or white space (token.is_space).
2 An alternative way to do this would be to first get a count of lemmas using the count_by() method of a spaCy document, and then filtering out punctuation if desired: counts = doc.count_by(spacy.attrs.LEMMA) (but then you’d have to map the resulting attributes (integers) back to words by looping over the tokens and checking their orth attribute).

Make it a script

You can play with the functions we’ve defined above by typing (copy-and-pasting) them into an interactive Python session. If we save them all to a file, then that file is a Python module which we could import and use in a Python script. To use a single file as both a module and a script, our file can include a construct like this:

if __name__ == "__main__":
    # our script logic here

This works because when the Python interpreter executes a script (as opposed to importing a module), it sets the top-level variable __name__ equal to the string "__main__" (see also: What does if __name__ == “__main__”: do?).

In our case, our script logic consists of reading any input files if given, running all of our hapax functions, then collecting and displaying the output. To see how it is done, scroll down to the full program listing below.

Running it

To run the script, first download and save hapaxes.py. Then:

$ python hapaxes.py

Depending on which NLP packages you have installed, you should see output like:

               Count
     Wordforms   9
    NLTK-stems   8
   NLTK-lemmas   8
         spaCy   8

-- Hapaxes --
Wordforms:    careful, cautious, corrupt, if, not, now, see, that, you
NLTK-stems:   care, cautious, if, not, now, see, that, you
NLTK-lemmas:  care, cautious, if, not, now, see, that, you
spaCy:        careful, cautious, if, not, now, see, that, you

Try also running the script on an arbitrary input file:

$ python hapaxes.py somefilename

# run it on itself and note that
# source code doesn't give great results:
$ python hapaxes.py hapaxes.py

hapaxes.py listing

The entire script is listed below and available at hapaxes.py.

hapaxes.py
  1"""
  2A sample script/module which demonstrates how to count hapaxes (tokens which
  3appear only once) in an untagged text corpus using plain python, NLTK, and
  4spaCy. It counts and lists hapaxes in five different ways:
  5
  6    * Wordforms - counts unique spellings (normalized for case). This uses
  7    plain Python (no NLTK required)
  8
  9    * NLTK stems - counts unique stems using a stemmer provided by NLTK
 10
 11    * NLTK lemmas - counts unique lemma forms using NLTK's part of speech
 12    * tagger and interface to the WordNet lemmatizer.
 13
 14    * spaCy lemmas - counts unique lemma forms using the spaCy NLP module.
 15
 16Each of the NLP modules (nltk, spaCy) are optional; if one is not
 17installed then its respective hapax-counting method will not be run.
 18
 19Usage:
 20
 21    python hapaxes.py [file]
 22
 23If 'file' is given, its contents are read and used as the text in which to
 24find hapaxes. If 'file' is omitted, then a test text will be used.
 25
 26Example:
 27
 28Running this script with no arguments:
 29
 30    python hapaxes.py
 31
 32Will process this text:
 33
 34    Cory Linguist, a cautious corpus linguist, in creating a corpus of
 35    courtship correspondence, corrupted a crucial link. Now, if Cory Linguist,
 36    a careful corpus linguist, in creating a corpus of courtship
 37    correspondence, corrupted a crucial link, see that YOU, in creating a
 38    corpus of courtship correspondence, corrupt not a crucial link.
 39
 40And produce this output:
 41
 42                Count
 43         Wordforms   9
 44             Stems   8
 45            Lemmas   8
 46             spaCy   8
 47
 48    -- Hapaxes --
 49    Wordforms:    careful, cautious, corrupt, if, not, now, see, that, you
 50    NLTK-stems:   care, cautious, if, not, now, see, that, you
 51    NLTK-lemmas:  care, cautious, if, not, now, see, that, you
 52    spaCy:        careful, cautious, if, not, now, see, that, you
 53
 54
 55Notice that the stems and lemmas methods do not count "corrupt" as a hapax
 56because it also occurs as "corrupted". Notice also that "Linguist" is not
 57counted as the text is normalized for case.
 58
 59See also the Wikipedia entry on "Hapex legomenon"
 60(https://en.wikipedia.org/wiki/Hapax_legomenon)
 61"""
 62
 63### Imports
 64#
 65# Import some Python 3 features to use in Python 2
 66from __future__ import print_function
 67from __future__ import unicode_literals
 68
 69# gives us access to command-line arguments
 70import sys
 71
 72# The Counter collection is a convenient layer on top of
 73# python's standard dictionary type for counting iterables.
 74from collections import Counter
 75
 76# The standard python regular expression module:
 77import re
 78
 79try:
 80    # Import NLTK if it is installed
 81    import nltk
 82
 83    # This imports NLTK's implementation of the Snowball
 84    # stemmer algorithm
 85    from nltk.stem.snowball import SnowballStemmer
 86
 87    # NLTK's interface to the WordNet lemmatizer
 88    from nltk.stem.wordnet import WordNetLemmatizer
 89except ImportError:
 90    nltk = None
 91    print("NLTK is not installed, so we won't use it.")
 92
 93try:
 94    # Import spaCy if it is installed
 95    import spacy
 96except ImportError:
 97    spacy = None
 98    print("spaCy is not installed, so we won't use it.")
 99
100def normalize_tokenize(string):
101    """
102    Takes a string, normalizes it (makes it lowercase and
103    removes punctuation), and then splits it into a list of
104    words.
105
106    Note that everything in this function is plain Python
107    without using NLTK (although as noted below, NLTK provides
108    some more sophisticated tokenizers we could have used).
109    """
110    # make lowercase
111    norm = string.lower()
112
113    # remove punctuation
114    norm = re.sub(r'(?u)[^\w\s]', '', norm) # <1>
115
116    # split into words
117    tokens = norm.split()
118
119    return tokens
120
121def word_form_hapaxes(tokens):
122    """
123    Takes a list of tokens and returns a list of the
124    wordform hapaxes (those wordforms that only appear once)
125
126    For wordforms this is simple enough to do in plain
127    Python without an NLP package, especially using the Counter
128    type from the collections module (part of the Python
129    standard library).
130    """
131
132    counts = Counter(tokens) # <1>
133    hapaxes = [word for word in counts if counts[word] == 1] # <2>
134
135    return hapaxes
136
137def nltk_stem_hapaxes(tokens):
138    """
139    Takes a list of tokens and returns a list of the word
140    stem hapaxes.
141    """
142    if not nltk: # <1>
143        # Only run if NLTK is loaded
144        return None
145
146    # Apply NLTK's Snowball stemmer algorithm to tokens:
147    stemmer = SnowballStemmer("english")
148    stems = [stemmer.stem(token) for token in tokens]
149
150    # Filter down to hapaxes:
151    counts = nltk.FreqDist(stems) # <2>
152    hapaxes = counts.hapaxes() # <3>
153    return hapaxes
154
155def nltk_lemma_hapaxes(tokens):
156    """
157    Takes a list of tokens and returns a list of the lemma
158    hapaxes.
159    """
160    if not nltk:
161        # Only run if NLTK is loaded
162        return None
163
164    # Tag tokens with part-of-speech:
165    tagged = nltk.pos_tag(tokens) # <1>
166
167    # Convert our Treebank-style tags to WordNet-style tags.
168    tagged = [(word, pt_to_wn(tag))
169                     for (word, tag) in tagged] # <2>
170
171    # Lemmatize:
172    lemmer = WordNetLemmatizer()
173    lemmas = [lemmer.lemmatize(token, pos)
174                     for (token, pos) in tagged] # <3>
175
176    return nltk_stem_hapaxes(lemmas) # <4>
177
178def pt_to_wn(pos):
179    """
180    Takes a Penn Treebank tag and converts it to an
181    appropriate WordNet equivalent for lemmatization.
182
183    A list of Penn Treebank tags is available at:
184    https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html
185    """
186
187    from nltk.corpus.reader.wordnet import NOUN, VERB, ADJ, ADV
188
189    pos = pos.lower()
190
191    if pos.startswith('jj'):
192        tag = ADJ
193    elif pos == 'md':
194        # Modal auxiliary verbs
195        tag = VERB
196    elif pos.startswith('rb'):
197        tag = ADV
198    elif pos.startswith('vb'):
199        tag = VERB
200    elif pos == 'wrb':
201        # Wh-adverb (how, however, whence, whenever...)
202        tag = ADV
203    else:
204        # default to NOUN
205        # This is not strictly correct, but it is good
206        # enough for lemmatization.
207        tag = NOUN
208
209    return tag
210
211def spacy_hapaxes(rawtext):
212    """
213    Takes plain text and returns a list of lemma hapaxes using
214    the spaCy NLP package.
215    """
216    if not spacy:
217        # Only run if spaCy is installed
218        return None
219
220    # Load the English spaCy parser
221    spacy_parse = spacy.load('en_core_web_sm')
222
223    # Tokenize, parse, and tag text:
224    doc = spacy_parse(rawtext)
225
226    lemmas = [token.lemma_ for token in doc
227            if not token.is_punct and not token.is_space] # <1>
228
229    # Now we can get a count of every lemma:
230    counts = Counter(lemmas) # <2>
231
232    # We are interested in lemmas which appear only once
233    hapaxes = [lemma for lemma in counts if counts[lemma] == 1]
234    return hapaxes
235
236if __name__ == "__main__":
237    """
238    The code in this block is run when this file is executed as a script (but
239    not if it is imported as a module by another Python script).
240    """
241
242    # If no file is provided, then use this sample text:
243    text = """Cory Linguist, a cautious corpus linguist, in creating a
244    corpus of courtship correspondence, corrupted a crucial link. Now, if Cory
245    Linguist, a careful corpus linguist, in creating a corpus of courtship
246    correspondence, corrupted a crucial link, see that YOU, in creating a
247    corpus of courtship correspondence, corrupt not a crucial link."""
248
249    if len(sys.argv) > 1:
250        # We got at least one command-line argument. We'll ignore all but the
251        # first.
252        with open(sys.argv[1], 'r') as file:
253            text = file.read()
254            try:
255                # in Python 2 we need a unicode string
256                text = unicode(text)
257            except:
258                # in Python 3 'unicode()' is not defined
259                # we don't have to do anything
260                pass
261
262    # tokenize the text (break into words)
263    tokens = normalize_tokenize(text)
264
265    # Get hapaxes based on wordforms, stems, and lemmas:
266    wfs = word_form_hapaxes(tokens)
267    stems = nltk_stem_hapaxes(tokens)
268    lemmas = nltk_lemma_hapaxes(tokens)
269    spacy_lems = spacy_hapaxes(text)
270
271    # Print count table and list of hapaxes:
272    row_labels = ["Wordforms"]
273    row_data = [wfs]
274
275    # only add NLTK data if it is installed
276    if nltk:
277        row_labels.extend(["NLTK-stems", "NLTK-lemmas"])
278        row_data.extend([stems, lemmas])
279
280    # only add spaCy data if it is installed:
281    if spacy_lems:
282        row_labels.append("spaCy")
283        row_data.append(spacy_lems)
284
285    # sort happaxes for display
286    row_date = [row.sort() for row in row_data]
287
288    # format and print output
289    rows = zip(row_labels, row_data)
290    row_fmt = "{:>14}{:^8}"
291    print("\n")
292    print(row_fmt.format("", "Count"))
293    hapax_list = []
294    for row in rows:
295        print(row_fmt.format(row[0], len(row[1])))
296        hapax_list += ["{:<14}{:<68}".format(row[0] + ":", ", ".join(row[1]))]
297
298    print("\n-- Hapaxes --")
299    for row in hapax_list:
300        print(row)
301    print("\n")
302

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