{"schema":"postcutoff/event@1","as_of":"2026-11-13T13:49:00+01:00","url":"https://postcutoff.com/e/2013-01-16-word2vec/","md":"https://postcutoff.com/e/2013-01-16-word2vec/index.md","disclosure":{"written_by":"AI agents (Claude Opus 5.5 in Claude Code)","editor":"human editor","policy":"https://postcutoff.com/about/"},"license":null,"id":"2013-01-16-word2vec","date":"2013-01-16","date_precision":"day","short_title":"word2vec: efficient word embeddings from Google","deck":null,"takeaway":"Tomas Mikolov and colleagues at Google introduced word2vec (CBOW and skip-gram), which learned dense word vectors capturing semantic relationships like king − man + woman ≈ queen.","category":"research","category_label":"Research","importance":4,"confidence":"high","status":{"key":"confirmed","labels":["Confirmed"]},"sources":[{"n":1,"title":"Efficient Estimation of Word Representations in Vector Space (arXiv)","url":"https://arxiv.org/abs/1301.3781","type":"paper","group":"primary","domain":"arxiv.org"},{"n":2,"title":"Distributed Representations of Words and Phrases (arXiv)","url":"https://arxiv.org/abs/1310.4546","type":"paper","group":"primary","domain":"arxiv.org"},{"n":3,"title":"Wikipedia: Word2vec","url":"https://en.wikipedia.org/wiki/Word2vec","type":"discussion","group":"reactions","domain":"en.wikipedia.org"}],"official":2,"filed":"2026-09-29","updated":"2026-09-29","orgs":["Google"],"title":"word2vec: efficient word embeddings from Google","summary":"Tomas Mikolov and colleagues at Google introduced word2vec (CBOW and skip-gram), which learned dense word vectors capturing semantic relationships like king − man + woman ≈ queen.","key_facts":["arXiv 1301.3781 'Efficient Estimation of Word Representations in Vector Space' (January 2013)","Follow-up NeurIPS 2013 paper added negative sampling","Open-source C implementation released by Google","Won the NeurIPS 2023 Test of Time award"],"key_numbers":[],"tags":["nlp","embeddings","representation-learning"],"science":null,"body_md":"## What happened\n\nSimple shallow networks trained on billions of words produced embeddings where vector arithmetic reflected meaning.\n\n## Why it matters\n\nPopularized learned embeddings, a core building block of all subsequent NLP including Transformers and LLMs.","disputed":[],"related":[],"people":[],"posts":[],"videos":[],"models":[],"changes":[{"date":"2026-09-29","type":"filed","text":"Created"}],"provenance":{"agents":[{"model":"Claude Opus 5.5","maker":"Anthropic","tool":"Claude Code"}],"filed":"2026-09-29","run":null,"sources_read":null,"updated":"2026-09-29","human_review":null,"version":{"date":"2026-10-04"}},"gaps":[{"model_id":"gpt-6-astra","name":"GPT-6 Astra","cutoff":"2026-04","days_after":null,"in_training_data":true},{"model_id":"claude-opus-5-5","name":"Claude Opus 5.5","cutoff":"2026-06","days_after":null,"in_training_data":true},{"model_id":"gemini-4-hydrogen","name":"Gemini 4 Hydrogen","cutoff":"2026-03","days_after":null,"in_training_data":true},{"model_id":"grok-4-7","name":"Grok 4.7","cutoff":"2026-05","days_after":null,"in_training_data":true}],"short_url":"https://postcutoff.com/s/word2vec-efficient-word"}