This article explains the evolution of natural language processing, introduces transformer‑based large models such as BERT, GPT and T5, and details how words are represented through one‑hot vectors and dense word embeddings, illustrating their training and analogy capabilities.
The article explains distributional semantics in NLP, describing how the distributional hypothesis links word meaning to context, how co‑occurrence matrices are built from example sentences, why these matrices are large and sparse, and how SVD‑based LSA reduces them to dense word vectors.
This article explains the fundamentals of word vectors in NLP, walks through constructing them via co‑occurrence matrices and dimensionality reduction, demonstrates the process with a concrete example and Python code, and evaluates the resulting embeddings using cosine similarity.
This article reviews the latest ACL research on deep‑learning‑driven natural‑language processing, covering advances in spoken dialogue policy optimization, retrieval‑based chatbots, information extraction, sentiment analysis, syntactic parsing efficiency, and word‑ and sentence‑vector techniques, highlighting key papers, datasets, and future challenges.
