This article explains how language models evolved from character‑level embeddings to word‑level and finally to sub‑word tokenizers, highlighting the efficiency, vocabulary coverage, and practical engineering challenges of sub‑word segmentation in modern AI systems.
An in‑depth Q&A breaks down core BERT concepts—from the purpose of the [CLS] token and masking strategies to self‑attention complexity, sparse attention tricks, subword handling of OOV words, warm‑up learning rates, GPT’s unidirectional nature, and ALBERT’s parameter sharing—providing concise explanations for each.
This article provides a comprehensive overview of natural language processing fundamentals, covering the challenges of language modeling, N‑gram and Markov assumptions, smoothing techniques such as discounting and add‑one, evaluation via perplexity, basic tasks like Chinese word segmentation, subword tokenization, POS tagging, syntactic and semantic parsing, and a range of downstream applications including information extraction, sentiment analysis, question answering, machine translation, and dialogue systems.
