Cosine similarity is a powerful metric with wide-ranging applications in natural language processing, information retrieval, recommendation systems, and more. It enables us to quantify the similarity or dissimilarity between two non-zero vectors in a multi-dimensional space. In the realm of text data, cosine similarity plays a vital role in measuring the similarity between documents or sentences.

In this blog post, we will explore cosine similarity and its applications using the SpaCy library. We'll delve into the fundamental concept of cosine similarity, and show you how to compute it using Spacy. The tutorial covers:

- The concept of cosine similarity
- Installing SpaCy
- Computing Cosine Similarity with SpaCy
- Conclusion

Let's get started.

**The concept of cosine similarity**

Cosine similarity is a metric used to measure how similar two non-zero
vectors are in a multi-dimensional space. It's often employed in various
fields, including natural language processing, document retrieval, and
recommendation systems. Cosine similarity quantifies the cosine of the
angle between two vectors, and it ranges from -1 (completely dissimilar)
to 1 (completely similar), with 0 indicating no similarity.

Here's how cosine similarity works in the context of text data, which is a common application:

**Vector Representation**:
Documents or sentences are represented as vectors in a
multi-dimensional space. Each dimension typically corresponds to a word
or a term, and the value of each dimension represents the importance or
frequency of that word in the document. One common vectorization
technique is TF-IDF (Term Frequency-Inverse Document Frequency).

**Cosine of the Angle**:
The cosine similarity between two vectors is computed by taking the dot
product of the vectors and dividing it by the product of their
magnitudes (lengths). Mathematically, it's defined as:

Where:

A ⋅ B is the dot product of vectors A and B.

||A|| and ||B|| are the magnitudes (lengths) of vectors A and B.

**Interpreting the Result**:
If the vectors are identical (point in the same direction), the cosine
similarity is 1, indicating they are perfectly similar. If the vectors
are orthogonal (at a 90-degree angle), the cosine similarity is 0,
indicating no similarity. If the vectors are diametrically opposed
(point in opposite directions), the cosine similarity is -1, indicating
they are completely dissimilar.

**Installing SpaCy **

Before we dive into the code, you'll need to install SpaCy, a popular Python library for natural language processing. You can install it using pip:

Once installed, you'll need to download a language model for SpaCy. For
this example, we'll use the English model, but SpaCy supports multiple
languages, and you can choose the one that suits your needs. Download
the English model with:

**Computing cosine similarity with SpaCy **

Now, let's see how to compute cosine similarity with SpaCy. We'll use the en_core_web_sm model for text processing and compute the similarity between an input phrase and a set of standard phrases. We first load the English language model with SpaCy, and then we process the input phrase and standard phrases using the model. We compute cosine similarity using the vectors associated with these processed documents. The result will be the cosine similarity scores for each standard phrase.

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And result looks as below.

Similarity between 'Fix all bugs quickly.' and 'Debug with patience': 0.1817

Similarity between 'Fix all bugs quickly.' and 'Automate task': 0.1789

Similarity between 'Fix all bugs quickly.' and 'Think logically': 0.2591

**Conclusion**

**References:**

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