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Algorithms and Data Structures with Python

Project 4: Plagiarism Detection System

Handling Larger Documents and Paragraph-Level Analysis

For larger documents, analyzing the entire content at once might not be efficient or effective. Instead, we can break down the documents into smaller chunks, such as paragraphs or sentences, and compare these individually.

Chunking the Text:

Divide the document into smaller parts (paragraphs or sentences) for a more granular comparison.This approach can help identify specific sections where plagiarism might have occurred.

Example Code - Chunking Text:

def chunk_text(text, chunk_size):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

# Example Usage
large_text = preprocess_text("Your large document text goes here...")
chunks = chunk_text(large_text, 100)  # Chunking text into segments of 100 words

Comparing Text Chunks

Apply the cosine similarity measure (or another similarity algorithm) to each pair of text chunks from the two documents.

Aggregate the similarity scores to determine the overall similarity.

Example Code - Comparing Chunks:

def compare_chunks(chunks1, chunks2):
    total_similarity = 0
    comparisons = 0

    for chunk1 in chunks1:
        for chunk2 in chunks2:
            similarity = cosine_similarity(chunk1, chunk2)
            total_similarity += similarity
            comparisons += 1

    average_similarity = total_similarity / comparisons if comparisons > 0 else 0
    return average_similarity

# Example Usage
chunks_doc1 = chunk_text(preprocess_text("Document 1 text..."), 100)
chunks_doc2 = chunk_text(preprocess_text("Document 2 text..."), 100)
print(compare_chunks(chunks_doc1, chunks_doc2))  # Output: Average similarity score

Handling Larger Documents and Paragraph-Level Analysis

For larger documents, analyzing the entire content at once might not be efficient or effective. Instead, we can break down the documents into smaller chunks, such as paragraphs or sentences, and compare these individually.

Chunking the Text:

Divide the document into smaller parts (paragraphs or sentences) for a more granular comparison.This approach can help identify specific sections where plagiarism might have occurred.

Example Code - Chunking Text:

def chunk_text(text, chunk_size):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

# Example Usage
large_text = preprocess_text("Your large document text goes here...")
chunks = chunk_text(large_text, 100)  # Chunking text into segments of 100 words

Comparing Text Chunks

Apply the cosine similarity measure (or another similarity algorithm) to each pair of text chunks from the two documents.

Aggregate the similarity scores to determine the overall similarity.

Example Code - Comparing Chunks:

def compare_chunks(chunks1, chunks2):
    total_similarity = 0
    comparisons = 0

    for chunk1 in chunks1:
        for chunk2 in chunks2:
            similarity = cosine_similarity(chunk1, chunk2)
            total_similarity += similarity
            comparisons += 1

    average_similarity = total_similarity / comparisons if comparisons > 0 else 0
    return average_similarity

# Example Usage
chunks_doc1 = chunk_text(preprocess_text("Document 1 text..."), 100)
chunks_doc2 = chunk_text(preprocess_text("Document 2 text..."), 100)
print(compare_chunks(chunks_doc1, chunks_doc2))  # Output: Average similarity score

Handling Larger Documents and Paragraph-Level Analysis

For larger documents, analyzing the entire content at once might not be efficient or effective. Instead, we can break down the documents into smaller chunks, such as paragraphs or sentences, and compare these individually.

Chunking the Text:

Divide the document into smaller parts (paragraphs or sentences) for a more granular comparison.This approach can help identify specific sections where plagiarism might have occurred.

Example Code - Chunking Text:

def chunk_text(text, chunk_size):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

# Example Usage
large_text = preprocess_text("Your large document text goes here...")
chunks = chunk_text(large_text, 100)  # Chunking text into segments of 100 words

Comparing Text Chunks

Apply the cosine similarity measure (or another similarity algorithm) to each pair of text chunks from the two documents.

Aggregate the similarity scores to determine the overall similarity.

Example Code - Comparing Chunks:

def compare_chunks(chunks1, chunks2):
    total_similarity = 0
    comparisons = 0

    for chunk1 in chunks1:
        for chunk2 in chunks2:
            similarity = cosine_similarity(chunk1, chunk2)
            total_similarity += similarity
            comparisons += 1

    average_similarity = total_similarity / comparisons if comparisons > 0 else 0
    return average_similarity

# Example Usage
chunks_doc1 = chunk_text(preprocess_text("Document 1 text..."), 100)
chunks_doc2 = chunk_text(preprocess_text("Document 2 text..."), 100)
print(compare_chunks(chunks_doc1, chunks_doc2))  # Output: Average similarity score

Handling Larger Documents and Paragraph-Level Analysis

For larger documents, analyzing the entire content at once might not be efficient or effective. Instead, we can break down the documents into smaller chunks, such as paragraphs or sentences, and compare these individually.

Chunking the Text:

Divide the document into smaller parts (paragraphs or sentences) for a more granular comparison.This approach can help identify specific sections where plagiarism might have occurred.

Example Code - Chunking Text:

def chunk_text(text, chunk_size):
    words = text.split()
    return [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]

# Example Usage
large_text = preprocess_text("Your large document text goes here...")
chunks = chunk_text(large_text, 100)  # Chunking text into segments of 100 words

Comparing Text Chunks

Apply the cosine similarity measure (or another similarity algorithm) to each pair of text chunks from the two documents.

Aggregate the similarity scores to determine the overall similarity.

Example Code - Comparing Chunks:

def compare_chunks(chunks1, chunks2):
    total_similarity = 0
    comparisons = 0

    for chunk1 in chunks1:
        for chunk2 in chunks2:
            similarity = cosine_similarity(chunk1, chunk2)
            total_similarity += similarity
            comparisons += 1

    average_similarity = total_similarity / comparisons if comparisons > 0 else 0
    return average_similarity

# Example Usage
chunks_doc1 = chunk_text(preprocess_text("Document 1 text..."), 100)
chunks_doc2 = chunk_text(preprocess_text("Document 2 text..."), 100)
print(compare_chunks(chunks_doc1, chunks_doc2))  # Output: Average similarity score