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
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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] 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] 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.jsonEmbed this claim
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The record lists 2 cited source(s): arXiv (Mikolov, Chen, Corrado, Dean), Google. Each is shown below with a short excerpt and URL. The JSON record at https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json includes SourceScore-issued HMAC integrity metadata, not a public verification proof.
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cURL
curl https://sourcescore.org/api/v1/claims/4978f76d228a3db1.jsonJavaScript / 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()