SourceScore

Verified claim · AI-ML · 100% confidence

Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013).

Last verified 2026-05-16 · Methodology veritas-v0.1 · 4978f76d228a3db1

Structured fields

Subject
Word2Vec
Predicate
introduced_in_paper
Object
Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)
Confidence
100%
Tags
word2vec · embeddings · foundational · mikolov · 2013 · google · nlp

Sources (2)

  1. [1] preprint · arXiv (Mikolov, Chen, Corrado, Dean) · 2013-01-16

    Efficient Estimation of Word Representations in Vector Space
    We propose two novel model architectures for computing continuous vector representations of words from very large data sets.
  2. [2] docs · Google

    word2vec Google Code archive

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Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013). — SourceScore Claim 4978f76d228a3db1 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json

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cURL

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JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_word2vec_fact() -> dict: """Fetch the verified SourceScore claim for Word2Vec.""" r = httpx.get("https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json") return r.json()