The cosine similarity between marked and masked (using bigrams with padding), rounded to 1 decimal place is:Group of answer choices0.80.50.60.7

To answer this question, we first need to understand what cosine similarity is and how it is calculated.

Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space. It is defined to equal the cosine of the angle between them, which is also the same as the inner product of the same vectors normalized to both have length 1.

Here are the steps to calculate the cosine similarity between the words "marked" and "masked" using bigrams with padding:

1. First, we need to create bigrams for each word. Bigrams are pairs of consecutive letters in the word. We add padding to the start and end of the word, which is typically represented by the dollar sign ($). So, the bigrams for "marked" would be:$m, ma, ar, rk, ke, ed, d$. The bigrams for "masked" would be:$m, ma, as, sk, ke, ed, d$.

2. Next, we create vectors for each word. Each unique bigram across both words forms a dimension in our vector space. The frequency of each bigram in the word is the value for that dimension in the vector. For example, if our dimensions are [$m, ma, ar, rk, ke, ed, d$, as, sk], the vector for "marked" would be [1, 1, 1, 1, 1, 1, 1, 0, 0] and the vector for "masked" would be [1, 1, 0, 0, 1, 1, 1, 1, 1].

3. Now, we calculate the cosine similarity between these two vectors. The cosine similarity is the dot product of the two vectors divided by the product of the magnitudes of the two vectors.

4. Finally, we round the result to 1 decimal place.

Without the actual vectors, we can't calculate the exact cosine similarity. However, based on the options provided (0.8, 0.5, 0.6, 0.7), you would choose the one that matches your calculated result.