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https://github.com/rspeer/wordfreq.git
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42a7d5a439
commit
319c3abaab
@ -133,3 +133,45 @@ def write_wordlist(freqs, filename, cutoff=1e-8):
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break
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if not ('"' in word or ',' in word):
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writer.writerow([word, str(freq)])
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# APOSTROPHE_TRIMMED_PROB represents the probability that this word has had
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# "'t" removed from it, based on counts from Twitter, which we know
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# accurate token counts for based on our own tokenizer.
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APOSTROPHE_TRIMMED_PROB = {
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'don': 0.99,
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'didn': 1.,
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'can': 0.35,
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'won': 0.74,
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'isn': 1.,
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'wasn': 1.,
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'wouldn': 1.,
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'doesn': 1.,
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'couldn': 1.,
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'ain': 0.99,
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'aren': 1.,
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'shouldn': 1.,
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'haven': 0.96,
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'weren': 1.,
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'hadn': 1.,
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'hasn': 1.,
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'mustn': 1.,
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'needn': 1.,
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}
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def correct_apostrophe_trimming(freqs):
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"""
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If what we got was an English wordlist that has been tokenized with
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apostrophes as token boundaries, correct the spurious tokens we get by
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adding 't in about the proportion we expect to see in the wordlist.
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"""
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if (freqs.get('wouldn', 0) > 1e-6 and freqs.get('couldn', 0) > 1e-6):
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print("Applying apostrophe trimming")
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for trim_word, trim_prob in APOSTROPHE_TRIMMED_PROB.items():
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if trim_word in freqs:
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freq = freqs[trim_word]
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freqs[trim_word] = freq * (1 - trim_prob)
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freqs[trim_word + "'t"] = freq * trim_prob
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return freqs
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