Document similarity ranking in NLP

Introduction

Document similarity ranking helps find how close or related two texts are. It helps organize and find important documents quickly.

Finding similar news articles to a given article

Recommending research papers related to a topic

Grouping customer reviews that talk about the same issue

Searching for documents that match a user's query

Detecting duplicate or near-duplicate documents in a database

Syntax

NLP

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# List of documents
documents = ["text1", "text2", "text3"]

# Convert documents to vectors
vectorizer = TfidfVectorizer()
doc_vectors = vectorizer.fit_transform(documents)

# Compute similarity matrix
similarity_matrix = cosine_similarity(doc_vectors)

TfidfVectorizer converts text into numbers that show importance of words.

cosine_similarity measures how close two vectors are, from 0 (not similar) to 1 (exactly similar).

Examples

This example shows similarity scores between three simple sentences.

NLP

documents = ["I love apples", "I like oranges", "Apples and oranges are fruits"]
vectorizer = TfidfVectorizer()
doc_vectors = vectorizer.fit_transform(documents)
similarity_matrix = cosine_similarity(doc_vectors)
print(similarity_matrix)

This shows how to find similarity scores of a new query against existing documents.

NLP

query = "I enjoy apples"
query_vec = vectorizer.transform([query])
scores = cosine_similarity(query_vec, doc_vectors)
print(scores)

Sample Model

This program converts four documents into number vectors and calculates how similar each document is to the others. It prints a matrix showing similarity scores between 0 and 1.

NLP

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Sample documents
documents = [
    "Machine learning is fun",
    "Artificial intelligence and machine learning",
    "I love reading about AI",
    "The sky is blue and beautiful"
]

# Convert documents to TF-IDF vectors
vectorizer = TfidfVectorizer()
doc_vectors = vectorizer.fit_transform(documents)

# Compute similarity matrix
similarity_matrix = cosine_similarity(doc_vectors)

# Print similarity matrix rounded to 2 decimals
for i, row in enumerate(similarity_matrix):
    print(f"Document {i} similarities:", [round(score, 2) for score in row])

Important Notes

Higher similarity scores mean documents are more alike.

TF-IDF helps reduce the effect of common words like 'the' or 'is'.

Cosine similarity works well for text because it focuses on the angle between vectors, not their length.

Summary

Document similarity ranking helps find related texts by comparing their content.

Use TF-IDF to turn text into numbers that show word importance.

Cosine similarity measures how close two documents are, giving a score from 0 to 1.

Practice

1. What does document similarity ranking help us do in natural language processing?

easy

A. Find how related two texts are based on their content

B. Translate documents into different languages

C. Summarize long documents into short ones

D. Detect spelling errors in documents

Solution

1. Step 1: Understand the purpose of document similarity ranking

   Document similarity ranking is used to compare texts and find how closely related they are based on their content.

2. Step 2: Identify the correct description

   Among the options, only finding relatedness of texts matches the purpose of document similarity ranking.

3. Final Answer:

   Find how related two texts are based on their content -> Option A

4. Quick Check:

   Document similarity ranking = Find related texts [OK]

Hint: Think: similarity means how close or related texts are [OK]

Common Mistakes:

• Confusing similarity ranking with translation
• Thinking it summarizes documents
• Mixing it up with spell checking

2. Which of the following is the correct way to compute cosine similarity between two vectors A and B in Python using NumPy?

easy

A. np.dot(A, B) * (np.linalg.norm(A) + np.linalg.norm(B))

B. np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B))

C. np.dot(A, B) - (np.linalg.norm(A) * np.linalg.norm(B))

D. np.dot(A, B) / (np.linalg.norm(A) + np.linalg.norm(B))

Solution

1. Step 1: Recall cosine similarity formula

   Cosine similarity = dot product of vectors divided by product of their magnitudes (norms).

2. Step 2: Match formula to code

   np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) correctly implements this formula using np.dot and np.linalg.norm.

3. Final Answer:

   np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) -> Option B

4. Quick Check:

   Cosine similarity formula = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) [OK]

Hint: Cosine similarity = dot product ÷ (norm A x norm B) [OK]

Common Mistakes:

• Adding norms instead of multiplying
• Subtracting norms instead of dividing
• Multiplying dot product by sum of norms

3. Given the following Python code using TF-IDF and cosine similarity, what will be the printed similarity score between the two documents?

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

docs = ['apple orange banana', 'banana fruit apple']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)
sim_score = cosine_similarity(X[0], X[1])[0][0]
print(round(sim_score, 2))

medium

A. 0.50

B. 1.00

C. 0.58

D. 0.00

Solution

1. Step 1: Understand TF-IDF vectorization of similar documents

   Both documents share words 'apple' and 'banana' and have similar content, so their TF-IDF vectors will be close.

2. Step 2: Calculate cosine similarity between vectors

   Cosine similarity between these vectors will be high but less than 1, approximately 0.58 after rounding.

3. Final Answer:

   0.58 -> Option C

4. Quick Check:

   Similarity of similar docs ≈ 0.58 [OK]

Hint: Similar docs have cosine similarity close to 1 but not exactly 1 [OK]

Common Mistakes:

• Assuming similarity is exactly 1 for similar texts
• Confusing cosine similarity with Euclidean distance
• Ignoring TF-IDF weighting effects

4. The following code attempts to compute cosine similarity between two documents but raises an error. What is the main issue?

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

docs = ['cat dog', 'dog mouse']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs).toarray()
sim_score = cosine_similarity(X[0], X[1])
print(sim_score)

medium

A. cosine_similarity expects 2D arrays, but X[0] and X[1] are 1D arrays

B. TfidfVectorizer cannot process documents with different words

C. cosine_similarity requires dense arrays, not sparse matrices

D. The print statement has a typo in variable name

Solution

1. Step 1: Check input types for cosine_similarity

   cosine_similarity expects 2D arrays, but X[0] and X[1] are 1D arrays (shape (n_features,)).

2. Step 2: Understand how to fix the error

   Use X[0:1] and X[1:2] or reshape them properly to avoid the error.

3. Final Answer:

   cosine_similarity expects 2D arrays, but X[0] and X[1] are 1D arrays -> Option A

4. Quick Check:

   cosine_similarity input shape = 2D arrays [OK]

Hint: cosine_similarity needs 2D arrays, not single vectors [OK]

Common Mistakes:

• Thinking TfidfVectorizer fails on different words
• Thinking cosine_similarity accepts 1D arrays
• Overlooking variable name typos

5. You have a collection of 3 documents: ['apple banana', 'banana orange', 'apple orange banana']. You want to rank these documents by similarity to the query 'banana apple'. Which approach correctly ranks them from most to least similar using TF-IDF and cosine similarity?

hard

A. Use raw word counts without TF-IDF, rank by Euclidean distance ascending

B. Count word overlaps between query and documents, rank by overlap count ascending

C. Compute TF-IDF vectors but rank by cosine similarity scores ascending

D. Compute TF-IDF vectors for all documents and query, then rank by cosine similarity scores descending

Solution

1. Step 1: Understand ranking by similarity

   To rank documents by similarity to a query, compute vector representations and measure similarity scores, then sort descending (highest similarity first).

2. Step 2: Identify correct method

   TF-IDF vectors and cosine similarity are standard; ranking by descending cosine similarity scores is correct.

3. Final Answer:

   Compute TF-IDF vectors for all documents and query, then rank by cosine similarity scores descending -> Option D

4. Quick Check:

   Similarity ranking = cosine similarity descending [OK]

Hint: Rank documents by highest cosine similarity to query [OK]

Common Mistakes:

• Ranking by ascending similarity (lowest first)
• Using raw counts without weighting
• Ranking by overlap count ascending