HW1 tests word vectors on the 19,544 Google Analogy questions (8,869 semantic, 10,675 syntactic). You first answer them with pretrained glove-wiki-gigaword-100 loaded through Gensim, then train your own Word2Vec on a 20% sample of a pre-cleaned Wikipedia dump, and plot t-SNE for the family subcategory both times. Seven TODOs are worth 55%, the report 45%. Fall 2026 keeps the same TODOs but asks for an .ipynb with outputs.
Week 1 of Hung-Yu Kao's NLP course at NTHU is a 91-page deck, W1_NLP_brief. It opens with 'Watch for kids', five readings of the telescope sentence, and a Chinese tongue-twister about eleven uncles to show why language is hard. Then, from an information-retrieval angle, it builds one pipeline: inverted index, tokenization, stemming, TF-IDF, BM25. The second half hits that pipeline's dead ends (synonyms, polysemy, vocabulary mismatch), moves to SVD-based LSA, and closes with a preview of dense vectors through Skip-gram, GloVe, and FastText.
Lecture 2 moves from word2vec's prediction task, objective, and gradients to count-based vectors and evaluation. Meaning becomes a high-dimensional position learned from context, not a label retrieved from a dictionary.