I hope this doesn't make me sick..
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Xft2 | |
android | |
aspell | |
aspell-dict-en | |
auctex | |
autoconf | |
automake | |
bash | |
bash-completion | |
bzip2 |
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import pandas as pd | |
from gensim.models.word2vec import Word2Vec | |
words = pd.read_csv('wordsim.txt', header=None, names=('word1', 'word2', 'phonetic similarity')) | |
model = Word2Vec.load_word2vec_format('/GoogleNews-vectors-negative300.bin', binary=True) | |
def similarity(r): | |
try: |
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from colorsys import rgb_to_hls, hls_to_rgb | |
import random | |
def colors(): | |
r, g, b = 1, 0, 0 | |
h, l, s = rgb_to_hls(r, g, b) | |
while True: |
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[] | |
bin = ../../bin | |
corpora = %{bin}/corpora | |
fowler_corpora_py = %{bin}/fowler.corpora-py | |
bnc_corpus = bnc+ccg://${PWD}/CCG_BNC_v1 | |
# Get the wordsim353 similarity dataset | |
[data/wordsim353.csv] | |
recipe = |
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def words(path): | |
with open(path) as f: | |
for line in f: | |
for word in line.strip().split(): | |
yield ''.join(filter(str.isalpha, word.lower())) | |
def recursive(word, dictionary): | |
if not word: | |
return |
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p = nltk.parse.malt.MaltParser( | |
bin='/Users/dimazest/qmul/tools/parts/malt-1.7.2/dist/maltparser-1.7.2/maltparser-1.7.2.jar', | |
mco='engmalt.linear-1.7', | |
working_dir='/Users/dimazest/qmul/tools/sw/', | |
additional_java_args=['-Xmx512m'], | |
) | |
p.parse('a man runs'.split()).tree( |
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[circus] | |
statsd = True | |
httpd = True | |
httpd_host = localhost | |
httpd_port = 8081 | |
[watcher:poultry] | |
cmd = ./poultry filter -c poultry.cfg -s twitter://filter -v | |
singleton = True | |
stop_children = True |
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[rewrites] | |
github = | |
url ~ ^https://github.com/ | |
git@github.com: | |
kind = git |
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