32 KiB
32 KiB
import pandas as pd
columns = ['FileId','Paper', 'Idk1', 'Year','Idk2','Idk3', 'LeftContext', 'RightContext']
# dev_data = pd.read_csv('dev-0/in.tsv', sep='\t', names=columns, engine='python', quotechar='"', error_bad_lines=False)
# dev_expected = pd.read_csv('dev-0/expected.tsv', sep='\t', engine='python', quotechar='"', error_bad_lines=False)
dev_data = list()
with open('dev-0/in.tsv', "r") as f:
for line in f.readlines():
dev_data.append(line.split('\t')[-2])
dev_expected = list()
with open('dev-0/expected.tsv', "r") as f:
for line in f.readlines():
dev_expected.append(line.replace('\n',''))from nltk.tokenize import word_tokenize
def createBigram(data, expected):
listOfBigrams = []
bigramCounts = {}
unigramCounts = {}
for i in range(len(data)):
tokenized = word_tokenize(data[i])
word = tokenized[-1]
listOfBigrams.append((word, expected[i]))
if (word, expected[i]) in bigramCounts:
bigramCounts[(word, expected[i])] += 1
else:
bigramCounts[(word, expected[i])] = 1
if data[i] in unigramCounts:
unigramCounts[word] += 1
else:
unigramCounts[word] = 1
return listOfBigrams, unigramCounts, bigramCounts
def calcBigramProb(listOfBigrams, unigramCounts, bigramCounts):
listOfProb = {}
for bigram in listOfBigrams:
word1 = bigram[0]
word2 = bigram[1]
listOfProb[bigram] = (bigramCounts.get(bigram))/(unigramCounts.get(word1))
return listOfProbbigrams, uniCounts, biCounts = createBigram(dev_data, dev_expected)probs = calcBigramProb(bigrams, uniCounts, biCounts)
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