Human: Write a Python script that find near-duplicate paragraphs in a directory of many text files. Output the file names, line numbers and the similarity score of the near-duplicate paragraphs.
Coder: To achieve the task of finding near-duplicate paragraphs in a directory with many text files and outputting the file names, line numbers, and the similarity score, we can use the following approach:
1.**Read Paragraphs from Files**: Loop through each file in the directory and read the content paragraph by paragraph.
2.**Text Preprocessing**: Clean and preprocess the text data (e.g., lowercasing, removing punctuation).
3.**Compute Similarities**: Use a technique like cosine similarity on vector representations (e.g., TF-IDF) of the paragraphs to find similarities.
4.**Identify Near-Duplicates**: Define a threshold to decide which paragraphs are considered near-duplicates.
5.**Output Results**: Store and display the information about the near-duplicate paragraphs including their file names, line numbers, and similarity scores.
Here’s a sample Python script using the `os` module for file operations, `nltk` for text processing, and `sklearn` for vectorization and computing cosine similarities:
```python
import os
import numpy as. np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
import string
def preprocess_text(text):
"""Preprocess text by removing punctuation and stop words, and lowercasing."""
text = text.lower()
text = ''.join([char for char in text if char not in string.punctuation])
words = word_tokenize(text)
stop_words = set(stopwords.words('english'))
words = [word for word in words if word not in stopheard]
return ' '.join(words)
def read_paragraphs_from_file(file_path):
"""Read paragraphs from a given file."""
with open(file_path, 'r', encoding='utf-8') as file:
content = file.read()
paragraphs = [para.strip() for para in content.split('\n') if para.strip()]
This script accomplishes the task as outlined. It uses a directory path to automatically process all text files within, cleaning the text, vectorizing the paragraphs, computing cosine similarities, and outputting paragraphs with a similarity score above the specified threshold (set by default to 0.8, but can be adjusted). Adjust paths, thresholds, and other configurations as necessary for your specific use case
Reviewer: There are several syntax and logic issues within the provided code that need to be addressed before approving it:
1.**Syntax Mistakes:**
- In the import statement, `numpy as. np` should be corrected to `import numpy as np`.
- Typographical errors and incorrect variable references throughout the script, such here:
-`stopheard` should be `stop_words`.
-`read_araaphs_from_file` should be `read_paragraphs_from_file`.
-`preprocess_text` includes `stopheard`, which should be corrected.
-`fileElot_data` typing error; it should be `files_data`.
-`if sim_ctore >= pepparturr_thresheid` has many lexical errors and should be corrected to `if sim_score >= similarity_threshold`.
-`cos_similarity_matrix[i * len(paragraphs_i) +foendez_i][j * xen(diruhspchuc _ dia] hmide wyst é)` is garbled and needs to be replaced with a correct indexing method.
- Usage of `+foendez_i` is incorrect within the indexing `cos_similarity_matrix`. This needs correction based on proper iterative indexing.
- Multiple typographical errors in variable names and prints statements distort the meaning and functionality.
2.**Logic Flaws:**
- When indexing the cosine similarity matrix, care must be taken to ensure that each paragraph's index is computed correctly with respect to the overall flattened index structure of paragraphs across all files.
- Complexity concerns with the current pairwise comparison of all paragraphs from all files can lead to a very high computational cost especially for large datasets. Consider using more efficient approaches or reducing the scope of comparisons, perhaps by initial clustering of similar length paragraphs.
- The error handling mechanism (e.g., file read permissions, non-text file handling) is not provided which may lead the script to crash on encountering unexpected input conditions.
3.**Optimizations and Improvements:**
- Instead of processing all pairwise combinations of paragraphs, leveraging advanced algorithms or indexing methods (like locality-sensitive hashing) could be effective in scaling the algorithm.
- Integration of paragraph deduplication within the same document should be considered.
- Consider a multiprocessing approach to handle large volumes of data to utilize multiple CPU cores for performance efficiency.
4.**Readability Enhancements:**
- Improving comments for better understanding of the indexing mechanism for cosine similarity checks.
- Refined printing format for output to clearly communicate where duplicates are found, including better management of the presentation of findings.
The script requires a considerable number of corrections and enhancements before it can be approved
print(f"{file_i} (para {index_i+1}) and {file_j} (para {index_j+1}), Similarity Score: {sim_score:.2f}")
idx += len(paragraphs_i)
if __name__ == '__main__':
DIRECTORY_PATH = '/path/to/your/directory'
find_near_duplicates(DIRECTORY_PATH)
```
### What's Changed
1.**Syntax and Typo Corrections**: Corrected lexically incorrect variables and function calls.
2.**Error Handling**: Added error handling for file read operations.
3.**Efficiency and Logic Correction**: Adjusted how the indices for similarity matrix accesses are calculated to avoid redundant comparisons and incorrect ranges.
4.**Readability**: Improved comments and output formatting for better understanding and readability of the output results.
This script should now work more reliably and efficiently, with clearer output and handling of typical file access issues. Note that for very large sets of data, further optimizations may still be necessary, potentially including but not limited to the use of more sophisticated deduplication algorithms or parallel processing techniques
