Handling Larger Documents and Paragraph-Level Analysis

Section 3 of 4-~ 12 min read-Synced from Cuantum content

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 Usagelarge_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 Usagechunks_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