/natural-language-processing

name: Natural Language Processing
description: Build NLP applications using transformers library, BERT, GPT, text classification, named entity recognition, and sentiment analysis

Natural Language Processing

Overview

This skill provides comprehensive tools for building NLP applications using modern transformers, BERT, GPT, and classical NLP techniques for text classification, named entity recognition, sentiment analysis, and more.

When to Use

• Building text classification systems for sentiment analysis, topic categorization, or intent detection
• Extracting named entities (people, places, organizations) from unstructured text
• Implementing machine translation, text summarization, or question answering systems
• Processing and analyzing large volumes of textual data for insights
• Creating chatbots, virtual assistants, or conversational AI applications
• Fine-tuning pre-trained transformer models for domain-specific NLP tasks

NLP Core Tasks

• **Text Classification**: Sentiment, topic, intent classification
• **Named Entity Recognition**: Identifying people, places, organizations
• **Machine Translation**: Text translation between languages
• **Text Summarization**: Extracting key information
• **Question Answering**: Finding answers in documents
• **Text Generation**: Generating coherent text

Popular Models and Libraries

• **Transformers**: BERT, GPT, RoBERTa, T5
• **spaCy**: Industrial NLP pipeline
• **NLTK**: Classic NLP toolkit
• **Hugging Face**: Pre-trained models hub
• **PyTorch/TensorFlow**: Deep learning frameworks

Python Implementation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import re
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer, WordNetLemmatizer
import torch
from transformers import (AutoTokenizer, AutoModelForSequenceClassification,
                         AutoModelForTokenClassification, pipeline,
                         TextClassificationPipeline)
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import warnings
warnings.filterwarnings('ignore')

# Download required NLTK resources
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

print("=== 1. Text Preprocessing ===")

def preprocess_text(text, remove_stopwords=True, lemmatize=True):
    """Complete text preprocessing pipeline"""
    # Lowercase
    text = text.lower()

    # Remove special characters and digits
    text = re.sub(r'[^a-zA-Z\s]', '', text)

    # Tokenize
    tokens = word_tokenize(text)

    # Remove stopwords
    if remove_stopwords:
        stop_words = set(stopwords.words('english'))
        tokens = [t for t in tokens if t not in stop_words]

    # Lemmatize
    if lemmatize:
        lemmatizer = WordNetLemmatizer()
        tokens = [lemmatizer.lemmatize(t) for t in tokens]

    return tokens, ' '.join(tokens)

sample_text = "The quick brown foxes are jumping over the lazy dogs! Amazing performance."
tokens, processed = preprocess_text(sample_text)
print(f"Original: {sample_text}")
print(f"Processed: {processed}")
print(f"Tokens: {tokens}\n")

# 2. Text Classification with sklearn
print("=== 2. Traditional Text Classification ===")

# Sample data
texts = [
    "I love this product, it's amazing!",
    "This movie is fantastic and entertaining.",
    "Best purchase ever, highly recommended.",
    "Terrible quality, very disappointed.",
    "Worst experience, waste of money.",
    "Horrible service and poor quality.",
    "The food was delicious and fresh.",
    "Great atmosphere and friendly staff.",
    "Bad weather today, very gloomy.",
    "The book was boring and uninteresting."
]

labels = [1, 1, 1, 0, 0, 0, 1, 1, 0, 0]  # 1: positive, 0: negative

# TF-IDF vectorization
tfidf = TfidfVectorizer(max_features=100, ngram_range=(1, 2))
X_tfidf = tfidf.fit_transform(texts)

# Train classifier
clf = MultinomialNB()
clf.fit(X_tfidf, labels)

# Evaluate
predictions = clf.predict(X_tfidf)
print(f"Accuracy: {accuracy_score(labels, predictions):.4f}")
print(f"Precision: {precision_score(labels, predictions):.4f}")
print(f"Recall: {recall_score(labels, predictions):.4f}")
print(f"F1: {f1_score(labels, predictions):.4f}\n")

# 3. Transformer-based text classification
print("=== 3. Transformer-based Classification ===")

try:
    # Use Hugging Face transformers for sentiment analysis
    sentiment_pipeline = pipeline(
        "sentiment-analysis",
        model="distilbert-base-uncased-finetuned-sst-2-english"
    )

    test_sentences = [
        "This is a wonderful movie!",
        "I absolutely hate this product.",
        "It's okay, nothing special.",
        "Amazing quality and fast delivery!"
    ]

    print("Sentiment Analysis Results:")
    for sentence in test_sentences:
        result = sentiment_pipeline(sentence)
        print(f"  Text: {sentence}")
        print(f"  Sentiment: {result[0]['label']}, Score: {result[0]['score']:.4f}\n")

except Exception as e:
    print(f"Transformer model not available: {str(e)}\n")

# 4. Named Entity Recognition (NER)
print("=== 4. Named Entity Recognition ===")

try:
    ner_pipeline = pipeline(
        "ner",
        model="distilbert-base-uncased",
        aggregation_strategy="simple"
    )

    text = "Apple Inc. was founded by Steve Jobs in Cupertino, California."
    entities = ner_pipeline(text)

    print(f"Text: {text}")
    print("Entities:")
    for entity in entities:
        print(f"  {entity['word']}: {entity['entity_group']} (score: {entity['score']:.4f})")

except Exception as e:
    print(f"NER model not available: {str(e)}\n")

# 5. Word embeddings and similarity
print("\n=== 5. Word Embeddings and Similarity ===")

from sklearn.metrics.pairwise impor