Preprocessing & Chunking

Introduction

In the realm of Retrieval & Knowledge-Driven AI, preprocessing and chunking are critical steps that enhance the efficiency and accuracy of data retrieval systems. They involve transforming raw data into a structured format and segmenting it into manageable pieces.

1. Preprocessing

Preprocessing is the initial step in data handling, where raw data is cleaned and transformed. This ensures that the data is in a suitable format for further analysis and retrieval.

1.1 Key Concepts

• Data Cleaning: Removing errors, duplicates, and inconsistencies.
• Normalization: Scaling data to a standard range.
• Tokenization: Dividing text into individual words or phrases.

1.2 Common Preprocessing Steps

1.3 Code Example: Text Preprocessing

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from nltk.corpus import stopwords
import nltk
nltk.download('stopwords')

# Load data
data = pd.read_csv('data.csv')

# Remove missing values
data.dropna(inplace=True)

# Tokenization and Stopword Removal
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(data['text_column'])
            

2. Chunking

Chunking is the process of dividing data into smaller, more manageable pieces or chunks. This is particularly useful in Natural Language Processing (NLP) for better understanding and processing of text.

2.1 Key Concepts

• Chunk Size: Determines how much data to include in each chunk.
• Overlapping Chunks: Useful for context retention.
• Non-overlapping Chunks: For distinct information extraction.

2.2 Chunking Strategies

2.3 Code Example: Text Chunking

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)]

text = "This is a sample text for chunking demonstration."
chunks = chunk_text(text, 5)
print(chunks)  # Output: ['This is a sample text for', 'chunking demonstration.']
            

3. Best Practices

To ensure effective preprocessing and chunking, consider the following:

• Always validate your data before processing.
• Choose appropriate chunk sizes based on the task at hand.
• Regularly review preprocessing steps to adapt to new data.

4. FAQ