Natural Language Processing

Introduction to NLP

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

Text Preprocessing

Before feeding text into a neural network, it must be converted into numbers. This involves several steps:

1. Tokenization: Splitting text into words or subwords.
2. Stop-word Removal: Removing extremely common words (e.g., "the", "a") that carry little semantic meaning.
3. Stemming/Lemmatization: Reducing words to their root form (e.g., "running" -> "run").

Word Embeddings

Historically, words were represented as one-hot vectors, resulting in massive, sparse matrices. Today, we use dense Word Embeddings (like Word2Vec or GloVe) which map words to a continuous vector space where semantically similar words are placed close to each other.

from gensim.models import Word2Vec

# Train a simple Word2Vec model
sentences = [["machine", "learning", "is", "fascinating"], ["natural", "language", "processing"]]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)

# Get the vector for a word
vector = model.wv['machine']

The Transformer Architecture

Introduced in the paper "Attention Is All You Need" (2017), the Transformer architecture completely revolutionized NLP, replacing Recurrent Neural Networks (RNNs) and LSTMs.

Self-Attention

The core mechanism of the Transformer is Self-Attention. It allows the model to weigh the importance of different words in a sentence relative to a specific word, regardless of their positional distance. This solved the vanishing gradient problem in long sequences and allowed for massive parallelization during training.

Transformers form the backbone of all modern Large Language Models (LLMs) like GPT-4, BERT, and LLaMA!