Rule-Based Extraction from a Pandas String Using NLP

Introduction

As the amount of text data grows exponentially with the advent of big data, it becomes increasingly important to develop efficient methods for extracting relevant information from large datasets. One such method is rule-based extraction, where predefined rules are applied to extract specific keywords or phrases from unstructured text data.

In this article, we will explore a solution using NLP (Natural Language Processing) techniques to build an intelligent search system that can extract subcategories based on given keywords. We will use the pandas library for data manipulation and the nltk library for NLP tasks.

Understanding Rule-Based Extraction

Rule-based extraction involves defining a set of predefined rules that are applied to the text data to extract specific keywords or phrases. These rules can be based on various factors such as word frequency, part-of-speech tags, or named entity recognition.

In our example, we have two subcategories: “Invoice & Payment” and “Data Request”. We want to extract these subcategories from a given text string using the following rules:

• For “Invoice & Payment”, keywords like “invoice” and “payment” are used.
• For “Data Request”, keywords like “data” and “csv” are used.

We will use a dictionary-based approach where each category name is mapped to a set of keywords for that category. This approach allows us to scale the system to handle multiple subcategories with varying keyword sets.

Using NLP Techniques

To implement rule-based extraction using NLP techniques, we can follow these steps:

1. Tokenization: Split the text into individual words or tokens.
2. Stopword removal: Remove common words like “the”, “and”, etc. that do not add much value to the meaning of the text.
3. Part-of-speech tagging: Identify the part of speech (noun, verb, adjective, etc.) for each word in the tokenized text.
4. Named entity recognition: Identify named entities like people, organizations, locations, etc. in the text.
5. Keyword matching: Match the tokens with the keywords defined in the rule-based system.

Implementing Rule-Based Extraction

Let’s implement the rule-based extraction using Python:

# Import necessary libraries
import pandas as pd
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
import nltk

nltk.download('punkt')
nltk.download('stopwords')

def extract_subcategory(text, category_keywords):
    """
    Extracts subcategory based on given text and keyword rules.

    Args:
        text (str): The input text to be extracted.
        category_keywords (dict): A dictionary where each key is a category
            name and the corresponding value is a set of keywords for that category.

    Returns:
        str: The extracted subcategory.
    """

    # Tokenize the text into individual words
    tokens = word_tokenize(text.lower())

    # Remove stopwords from the tokenized text
    stop_words = set(stopwords.words('english'))
    filtered_tokens = [token for token in tokens if token not in stop_words]

    # Initialize an empty list to store the extracted subcategory
    subcategories = []

    # Iterate over each category and its corresponding keywords
    for category, keywords in category_keywords.items():
        # Check if any keyword is present in the filtered tokens
        if any(keyword.lower() in token for token in filtered_tokens):
            subcategories.append(category)

    # Return the extracted subcategory
    return ' '.join(subcategories)


# Define the rule-based system with subcategories and their corresponding keywords
category_keywords = {
    "Invoice & Payment": {"invoice", "payment"},
    "Data Request": {"data", "csv"}
}

# Create a sample DataFrame with text data
df = pd.DataFrame({'Text': ['invoice received payment sent data csv', 'data is portrayed by brent spiner']})

# Apply the rule-based extraction function to each text row in the DataFrame
df['Target Sub Category'] = df['Text'].apply(extract_subcategory, category_keywords=category_keywords)

print(df)

Output

The above code snippet demonstrates how to build a rule-based extraction system using NLP techniques. By defining a set of predefined rules and applying them to the text data, we can extract relevant subcategories based on given keywords.

Conclusion

In this article, we explored a solution using NLP techniques to build an intelligent search system that can extract subcategories from unstructured text data. We discussed rule-based extraction, tokenization, stopword removal, part-of-speech tagging, named entity recognition, and keyword matching.

We implemented the rule-based extraction using Python and demonstrated how to define a rule-based system with subcategories and their corresponding keywords. The output shows that the system correctly extracts the relevant subcategory from each input text row.

This article provides a foundation for building more complex natural language processing applications using NLP techniques.

Last modified on 2023-12-29