NLP elective assignments

assignment1.py

from nltk.corpus import brown
from nltk.util import ngrams
from collections import defaultdict

tagged_words = brown.tagged_words()

# question 1
def countTags(tag):
    count = 0
    for x in tagged_words:
        if x[1] == tag:
            count += 1
    return count

# question 2
def countWords(word):
    count = 0
    for x in tagged_words:
        if x[0] == word:
            count += 1
    return count

# question 3
def create_word_dictionary(our_word):
    word_counter = defaultdict(lambda: defaultdict(int))
    for word in tagged_words:
        word_counter[word[0]][word[1]] += 1
    return dict(word_counter[our_word])

# question 4
def create_tag_dictionary(tag):
    tags  =[]
    for x in tagged_words:
        tags.append(x[1])
    dd = defaultdict(int)
    bigrams=ngrams(tags, 2)
    for i in bigrams:
        if i[0] == "AT":
            dd[i[1]]+=1
    return dict(dd)

# question 5
def find_probablity(our_word,tag):
    word_counter = defaultdict(lambda: defaultdict(int))
    c_tot = 0
    tags  =[]
    for word in tagged_words:
        if word[1] == tag:
            c_tot+=1
        tags.append(word[1])
        word_counter[word[0]][word[1]] += 1
    val = dict(word_counter[our_word])
    c_word = 0
    for keys in val.keys():
        if keys == tag:
            c_word += val[keys]
    man_tt = c_word/c_tot
    print("prob = {}".format(man_tt))
    c_word_tag =0
    bigrams = ngrams(tags, 2)
    for i in bigrams:
        if i[0] == "AT" and i[1] == tag:
            c_word_tag += 1
    c_word_tag = c_word_tag/c_tot
    return man_tt * c_word_tag