I have a dictionary with close to a million multi-word terms (terms containing spaces). This looks something like
[...,
'multilayer ceramic',
'multilayer ceramic capacitor',
'multilayer optical disk',
'multilayer perceptron',
...]
I would like to count their frequency in many gigabytes of texts.
As a small example consider counting these four multi-word expressions in a Wikipedia page:
payload = {'action': 'query', 'titles': 'Ceramic_capacitor', 'explaintext':1, 'prop':'extracts', 'format': 'json'}
r = requests.get('https://en.wikipedia.org/w/api.php', params=payload)
sampletext = r.json()['query']['pages']['9221221']['extract'].lower()
sampledict = ['multilayer ceramic', 'multilayer ceramic capacitor', 'multilayer optical disk', 'multilayer perceptron']
termfreqdic = {}
for term in sampledict:
termfreqdic[term] = sampletext.count(term)
print(termfreqdic)
This gives something like {'multilayer ceramic': 7, 'multilayer ceramic capacitor': 2, 'multilayer optical disk': 0, 'multilayer perceptron': 0} but it seems sub-optimal if the dictionary contains a million entries.
I've tried with very large regular expressions:
termlist = [re.escape(w) for w in open('termlistfile.txt').read().strip().split('\n')]
termregex = re.compile(r'\b'+r'\b|\b'.join(termlist), re.I)
termfreqdic = {}
for i,li in enumerate(open(f)):
for m in termregex.finditer(li):
termfreqdic[m.group(0)]=termfreqdic.get(m.group(0),0)+1
open('counted.tsv','w').write('\n'.join([a+'\t'+v for a,v in termfreqdic.items()]))
This is dead slow (6 minutes for 1000 lines of text on a recent i7).
But if I use regex instead of re by replacing the first two lines, it goes down to around 12s per 1000 lines of text, which is still very slow for my needs:
termlist = open(termlistfile).read().strip().split('\n')
termregex = regex.compile(r"\L<options>", options=termlist)
...
Note that this does not do exactly what I want as one term may be a subterm of another as in the example 'multilayer ceramic' and 'multilayer ceramic capacitor' (which also excludes approaches of first tokenizing as in Find multi-word terms in a tokenized text in Python).
This looks like a common problem of sequence matching, in text corpora or also in genetic strings, that must have well-known solutions. Maybe it can be solved with some trie of words (I don't mind the initial compilation of the term list to be slow)? Alas, I don't seem to be looking for the right terms. Maybe someone can point me in the right direction?
@SidharthMacherla brought me on the right track (NLTK and tokenization), although his solution does not address the problem of multi-word expressions, which, moreover, might be overlapping.
In brief, the best method I've found is to subclass NLTK's MWETokenizer and to add a function for counting the multi-words using util.Trie:
import re, regex, timeit
from nltk.tokenize import MWETokenizer
from nltk.util import Trie
class FreqMWETokenizer(MWETokenizer):
"""A tokenizer that processes tokenized text and merges multi-word expressions
into single tokens.
"""
def __init__(self, mwes=None, separator="_"):
super().__init__(mwes, separator)
def freqs(self, text):
"""
:param text: A list containing tokenized text
:type text: list(str)
:return: A frequency dictionary with multi-words merged together as keys
:rtype: dict
:Example:
>>> tokenizer = FreqMWETokenizer([ mw.split() for mw in ['multilayer ceramic', 'multilayer ceramic capacitor', 'ceramic capacitor']], separator=' ')
>>> tokenizer.freqs("Gimme that multilayer ceramic capacitor please!".split())
{'multilayer ceramic': 1, 'multilayer ceramic capacitor': 1, 'ceramic capacitor': 1}
"""
i = 0
n = len(text)
result = {}
while i < n:
if text[i] in self._mwes:
# possible MWE match
j = i
trie = self._mwes
while j < n and text[j] in trie:
if Trie.LEAF in trie:
# success!
mw = self._separator.join(text[i:j])
result[mw]=result.get(mw,0)+1
trie = trie[text[j]]
j = j + 1
else:
if Trie.LEAF in trie:
# success!
mw = self._separator.join(text[i:j])
result[mw]=result.get(mw,0)+1
i += 1
else:
i += 1
return result
>>> tokenizer = FreqMWETokenizer([ mw.split() for mw in ['multilayer ceramic', 'multilayer ceramic capacitor', 'ceramic capacitor']], separator=' ')
>>> tokenizer.freqs("Gimme that multilayer ceramic capacitor please!".split())
{'multilayer ceramic': 1, 'multilayer ceramic capacitor': 1, 'ceramic capacitor': 1}
Here's the test suite with speed measures:
Counting 10k multi-word terms in 10m characters took 2 seconds with FreqMWETokenizer, 4 seconds with the MWETokenizer (a complete tokenization is also provided but no overlaps are counted), 150 seconds with the simple count method, and 1000 seconds with a large regex. Trying 100k multi-word terms in 100m characters remains doable with tokenizers not with counting or regex.
For testing, please find the two large sample files at https://mega.nz/file/PsVVWSzA#5-OHy-L7SO6fzsByiJzeBnAbtJKRVy95YFdjeF_7yxA
def freqtokenizer(thissampledict, thissampletext):
"""
This method uses the above FreqMWETokenizer's function freqs.
It captures overlapping multi-words
counting 1000 terms in 1000000 characters took 0.3222855870008061 seconds. found 0 terms from the list.
counting 10000 terms in 10000000 characters took 2.5309120759993675 seconds. found 21 terms from the list.
counting 100000 terms in 29467534 characters took 10.57763242800138 seconds. found 956 terms from the list.
counting 743274 terms in 29467534 characters took 25.613067482998304 seconds. found 10411 terms from the list.
"""
tokenizer = FreqMWETokenizer([mw.split() for mw in thissampledict], separator=' ')
thissampletext = re.sub(' +',' ', re.sub('[^\s\w\/\-\']+',' ',thissampletext)) # removing punctuation except /-'_
freqs = tokenizer.freqs(thissampletext.split())
return freqs
def nltkmethod(thissampledict, thissampletext):
""" This function first produces a tokenization by means of MWETokenizer.
This takes the biggest matching multi-word, no overlaps.
They could be computed separately on the dictionary.
counting 1000 terms in 1000000 characters took 0.34804968100070255 seconds. found 0 terms from the list.
counting 10000 terms in 10000000 characters took 3.9042628339993826 seconds. found 20 terms from the list.
counting 100000 terms in 29467534 characters took 12.782784996001283 seconds. found 942 terms from the list.
counting 743274 terms in 29467534 characters took 28.684293715999956 seconds. found 9964 terms from the list.
"""
termfreqdic = {}
tokenizer = MWETokenizer([mw.split() for mw in thissampledict], separator=' ')
thissampletext = re.sub(' +',' ', re.sub('[^\s\w\/\-\']+',' ',thissampletext)) # removing punctuation except /-'_
tokens = tokenizer.tokenize(thissampletext.split())
freqdist = FreqDist(tokens)
termsfound = set([t for t in freqdist.keys()]) & set(thissampledict)
for t in termsfound:termfreqdic[t]=freqdist[t]
return termfreqdic
def countmethod(thissampledict, thissampletext):
"""
counting 1000 in 1000000 took 0.9351876619912218 seconds.
counting 10000 in 10000000 took 91.92642056700424 seconds.
counting 100000 in 29467534 took 3185.7411157219904 seconds.
"""
termfreqdic = {}
for term in thissampledict:
termfreqdic[term] = thissampletext.count(term)
return termfreqdic
def regexmethod(thissampledict, thissampletext):
"""
counting 1000 terms in 1000000 characters took 2.298602456023218 seconds.
counting 10000 terms in 10000000 characters took 395.46084802100086 seconds.
counting 100000: impossible
"""
termfreqdic = {}
termregex = re.compile(r'\b'+r'\b|\b'.join(thissampledict))
for m in termregex.finditer(thissampletext):
termfreqdic[m.group(0)]=termfreqdic.get(m.group(0),0)+1
return termfreqdic
def timing():
"""
for testing, find the two large sample files at
https://mega.nz/file/PsVVWSzA#5-OHy-L7SO6fzsByiJzeBnAbtJKRVy95YFdjeF_7yxA
"""
sampletext=open("G06K0019000000.txt").read().lower()
sampledict=open("manyterms.lower.txt").read().strip().split('\n')
print(len(sampletext),'characters',len(sampledict),'terms')
for i in range(4):
for f in [freqtokenizer, nltkmethod, countmethod, regexmethod]:
start = timeit.default_timer()
thissampledict = sampledict[:1000*10**i]
thissampletext = sampletext[:1000000*10**i]
termfreqdic = f(thissampledict, thissampletext)
#termfreqdic = countmethod(thissampledict, thissampletext)
#termfreqdic = regexmethod(thissampledict, thissampletext)
#termfreqdic = nltkmethod(thissampledict, thissampletext)
#termfreqdic = freqtokenizer(thissampledict, thissampletext)
print('{f} counting {terms} terms in {characters} characters took {seconds} seconds. found {termfreqdic} terms from the list.'.format(f=f, terms=len(thissampledict), characters=len(thissampletext), seconds=timeit.default_timer()-start, termfreqdic=len({a:v for (a,v) in termfreqdic.items() if v})))
timing()