NLP | Custom corpus

In Natural Language Processing (NLP) a corpus refers to a large and structured collection of text that is used for training, testing or evaluating NLP models. A custom corpus is a specialized collection of text data tailored to meet the specific needs of a particular NLP task or application. By creating a custom corpus researchers and developers can focus on specific domain. In this article we will learn more about custom corpus.

Understanding Custom Corpus

A custom corpus is a set of text data that is manually or automatically gathered, processed and prepared to serve specific needs. Unlike general-purpose corpora like the Brown Corpus or Reuters Corpus which are widely used across different domains, a custom corpus is often domain-specific and built to improve the performance of NLP models in niche areas. It can be used for:

1. Domain-Specific Relevance: It helps in focusing the model's training on a specific domain. This results in better performance and understanding in specialized fields such as medical, legal, financial or social media contexts.
2. Improved Model Accuracy: When a model is trained on data that closely resembles the target application it can achieve better performance whether it's for classification, sentiment analysis, named entity recognition (NER) or other NLP tasks.
3. Capturing Specific Vocabulary: Many industries use domain-specific terminology, jargon or slang. For example, in medical field terms like "cardiologist" and "endocarditis" are used frequently. A custom corpus ensures that your NLP model understands and processes this specialized vocabulary correctly.
4. Better Handling of Language Variations: Custom corpora can include regional dialects, different writing styles or specialized communication methods. By training on such data NLP systems can perform better when dealing with these variations.

Implementing Custom Corpus in Python

In this we will create a simple custom corpus for NLP tasks. We will be using a dataset containing custom data with noise and we will preprocess and structure it so we can use it in NLP models.

1. Importing Necessary Libraries

For this we will import pandas, os, string, NLTK and scikit learn.

import os
import pandas as pd
import string
import nltk
nltk.download('punkt_tab')
nltk.download('stopwords')
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split

2. Loading the Data

You can download dataset from here.

corpus_df = pd.read_csv('/content/custom_corpus.csv')

corpus_df.head()

Output:

3. Preprocessing the Text

Here we will preprocess the data like converting text to lower, removing punctuation etc

• text.lower(): Converts the text to lowercase to maintain uniformity like "Hello" and "hello" will be treated the same.
• text.translate(str.maketrans('', '', string.punctuation)): Removes punctuation from the text using Python's string.punctuation.
• word_tokenize(text): Splits the text into individual words and tokens.
• stopwords.words('english'): Retrieves a list of common English stopwords like "is", "the", "in" that don't carry meaningful information for many NLP tasks.
• [word for word in tokens if word not in stop_words]: Filters out stopwords from the tokenized text.
• corpus_df['cleaned_text'] = corpus_df['text'].apply(preprocess_text): Applies the preprocess_text function to the text column of the DataFrame creating a new column cleaned_text that contains the cleaned text.

def preprocess_text(text):
    text = text.lower()
    
    text = text.translate(str.maketrans('', '', string.punctuation))
    
    tokens = word_tokenize(text)
    
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word not in stop_words]
    
    return ' '.join(tokens)

corpus_df['cleaned_text'] = corpus_df['text'].apply(preprocess_text)

4. Saving the Processed Data

corpus_df[['text', 'cleaned_text']].to_csv('processed_corpus.csv', index=False)

print("Processed corpus saved as 'processed_corpus.csv'.")
corpus_df.head()

Output:

We have cleaned and saved our custom corpus dataset. We can use this dataset for model making and training for various NLP task like sentiment analysis, text classification etc.