iskm/wordfreq
1
0
Fork 0
mirror of https://github.com/rspeer/wordfreq.git synced 2024-12-23 17:31:41 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

WIP: fix apostrophe trimming

Browse Source 
Former-commit-id: e39d345c4b
This commit is contained in:
Robyn Speer 2015-09-08 11:08:21 -04:00
parent 42a7d5a439
commit 319c3abaab
1 changed files with 42 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

42
wordfreq_builder/wordfreq_builder/word_counts.py
View File

@ -133,3 +133,45 @@ def write_wordlist(freqs, filename, cutoff=1e-8):
break
if not ('"' in word or ',' in word):
writer.writerow([word, str(freq)])
# APOSTROPHE_TRIMMED_PROB represents the probability that this word has had
# "'t" removed from it, based on counts from Twitter, which we know
# accurate token counts for based on our own tokenizer.
APOSTROPHE_TRIMMED_PROB = {
'don': 0.99,
'didn': 1.,
'can': 0.35,
'won': 0.74,
'isn': 1.,
'wasn': 1.,
'wouldn': 1.,
'doesn': 1.,
'couldn': 1.,
'ain': 0.99,
'aren': 1.,
'shouldn': 1.,
'haven': 0.96,
'weren': 1.,
'hadn': 1.,
'hasn': 1.,
'mustn': 1.,
'needn': 1.,
}
def correct_apostrophe_trimming(freqs):
"""
If what we got was an English wordlist that has been tokenized with
apostrophes as token boundaries, correct the spurious tokens we get by
adding 't in about the proportion we expect to see in the wordlist.
"""
if (freqs.get('wouldn', 0) > 1e-6 and freqs.get('couldn', 0) > 1e-6):
print("Applying apostrophe trimming")
for trim_word, trim_prob in APOSTROPHE_TRIMMED_PROB.items():
if trim_word in freqs:
freq = freqs[trim_word]
freqs[trim_word] = freq * (1 - trim_prob)
freqs[trim_word + "'t"] = freq * trim_prob
return freqs