Natural Language Processing (NLP) Tutorial

Natural Language Processing (NLP) helps machines to understand and process human languages either in text or audio form. It is used across a variety of applications from speech recognition to language translation and text summarization.

Basics

NLP helps machines understand and generate human language by analyzing structure, meaning and context in text or speech.

• Introduction to NLP
• Phases of NLP

Libraries

Some of natural language processing libraries include:

• NLTK (Natural Language Toolkit)
• spaCy
• TextBlob
• Transformers (by Hugging Face)
• Gensim
• NLP Libraries

Text Preprocessing Techniques

Preprocessing is an important to clean and prepare the raw text data for analysis. Common preprocessing steps include:

• Tokenization
• Stopword Removal
• Punctuation Removal
• Stemming
• Lemmatization
• Text Normalization
• Parts of Speech (POS) Tagging
• Parsing

Text Representation and Embedding Techniques

Machines require numerical input, so text must be converted into numbers (vectors).

Text Representation Techniques

It converts textual data into numerical vectors.

• One-Hot Encoding
• Bag of Words (BOW)
• Term Frequency-Inverse Document Frequency (TF-IDF)
• N-Gram Language Modeling
• Latent Semantic Analysis (LSA)
• Latent Dirichlet Allocation (LDA)

Text Embedding Techniques

It refers to methods that create dense vector representations of text that captures semantic meaning.

• Word Embedding: Word2Vec, GloVe, fastText
• Pre-Trained Embedding: ELMo, BERT
• Document Embedding: Doc2Vec
• Advanced Embeddings: RoBERTa, DistilBERT

Model Training

Once text is numeric, models are trained to learn patterns and perform NLP tasks.

Traditional Machine Learning

• Naive Bayes
• Logistic Regression
• SVM
• Random Forest

 Deep Learning Techniques

• Artificial Neural Networks (ANNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Seq2Seq Models
• Transformer Models

Pre-Trained Language Models

• GPT (Generative Pre-trained Transformer)
• Transformers XL
• T5 (Text-to-Text Transfer Transformer)
• Transfer Learning with Fine-tuning

NLP Tasks

Core NLP tasks that help machines understand, interpret and generate human language.

• Text Classification: Dataset for Text Classification, Naive Bayes, Logistic Regression, RNNs, CNNs
• Information Extraction Named Entity Recognition (NER), NLTK, Relationship Extraction, Word Sense Disambiguation (WSD)
• Sentiment Analysis: VADER, RNN, Opinion Mining
• Machine Translation: Statistical Machine Translation of Language, Machine Translation with Transformer
• Text Summarization: Hugging Face Model, Sumy
• Text Generation: Fnet, LSTM, HuggingFace Model
• Question Answering: Retrieval-Based QA, Generative QA

Applications

• Voice Assistants: Alexa, Siri and Google Assistant use NLP for voice recognition and interaction.
• Grammar and Text Analysis: Tools like Grammarly, Microsoft Word and Google Docs apply NLP for grammar checking.
• Information Extraction: Search engines like Google and DuckDuckGo use NLP to extract relevant information.
• Chatbots: Website bots and customer support chatbots leverage NLP for automated conversations.

For more details you can refer to: Applications of NLP