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
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
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
Cite this claim
Ready-to-paste citation (Markdown / plain text):
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
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/4978f76d228a3db1/" width="100%" height="360" frameborder="0" loading="lazy" title="Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
BERT (Bidirectional Encoder Representations from Transformers) introduced in paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin et al., 2018).
4c1ee70007dc89c1 · 100% confidence · shares 3 tags (foundational, google, nlp)
T5 (Text-to-Text Transfer Transformer) introduced in paper: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Raffel et al., 2019).
ef28341c3b308737 · 100% confidence · shares 2 tags (foundational, google)
Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).
2d6d7f61f1db6493 · 100% confidence · shares 2 tags (foundational, google)
Switch Transformer introduced in paper: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Fedus et al., 2021).
3d9c14b9379038c9 · 100% confidence · shares 2 tags (foundational, google)
Chain-of-Thought prompting introduced in paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022).
3af924da138ff84c · 100% confidence · shares 2 tags (foundational, google)
Frequently asked questions
Is the claim "Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "Word2Vec introduced in paper: Efficient Estimation of Word Representations in Vector Space (Mikolov et al., 2013)."?
Evidence comes from 2 primary sources: arXiv (Mikolov, Chen, Corrado, Dean), Google. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/4978f76d228a3db1.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
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()