Calculate cosine similarity given 2 sentence strings

Question

From Python: tf-idf-cosine: to find document similarity , it is possible to calculate document similarity using tf-idf cosine. Without importing external libraries, are that

Answer 1

Try this. Download the file 'numberbatch-en-17.06.txt' from https://conceptnet.s3.amazonaws.com/downloads/2017/numberbatch/numberbatch-en-17.06.txt.gz and extract it. The function 'get_sentence_vector' uses a simple sum of word vectors. However it can be improved by using weighted sum where weights are proportional to Tf-Idf of each word.

import math
import numpy as np

std_embeddings_index = {}
with open('path/to/numberbatch-en-17.06.txt') as f:
    for line in f:
        values = line.split(' ')
        word = values[0]
        embedding = np.asarray(values[1:], dtype='float32')
        std_embeddings_index[word] = embedding

def cosineValue(v1,v2):
    "compute cosine similarity of v1 to v2: (v1 dot v2)/{||v1||*||v2||)"
    sumxx, sumxy, sumyy = 0, 0, 0
    for i in range(len(v1)):
        x = v1[i]; y = v2[i]
        sumxx += x*x
        sumyy += y*y
        sumxy += x*y
    return sumxy/math.sqrt(sumxx*sumyy)


def get_sentence_vector(sentence, std_embeddings_index = std_embeddings_index ):
    sent_vector = 0
    for word in sentence.lower().split():
        if word not in std_embeddings_index :
            word_vector = np.array(np.random.uniform(-1.0, 1.0, 300))
            std_embeddings_index[word] = word_vector
        else:
            word_vector = std_embeddings_index[word]
        sent_vector = sent_vector + word_vector

    return sent_vector

def cosine_sim(sent1, sent2):
    return cosineValue(get_sentence_vector(sent1), get_sentence_vector(sent2))

I did run for the given sentences and found the following results

s1 = "This is a foo bar sentence ."
s2 = "This sentence is similar to a foo bar sentence ."
s3 = "What is this string ? Totally not related to the other two lines ."

print cosine_sim(s1, s2) # Should give high cosine similarity
print cosine_sim(s1, s3) # Shouldn't give high cosine similarity value
print cosine_sim(s2, s3) # Shouldn't give high cosine similarity value

0.9851735249068168
0.6570885718962608
0.6589335425458225