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
# Download reqed NLTK data packages
$ python -c 'import nltk; nltk.download("wordnet"); nltk.download("averaged_perceptron_tagger"); nltk.download("omw-1.4")'
# install spaCy:
$ pip install spacy
# install spaCy en model:
$ python -m spacy download en_core_web_smOptional 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:
### 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:
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.
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:
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.
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:
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 tagFinding 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. |
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 hereThis 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.pyDepending 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.pyhapaxes.py listing
The entire script is listed below and available at 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