Distributed Representations of Words and Phrases and Their Compositionality¶
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed Representations of Words and Phrases and Their Compositionality. Advances in Neural Information Processing Systems 26.
Cited by¶
5 citations across 5 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Embedding
- The guest is a discrete vocabulary of several hundred thousand tokens, whose native structure is semantic relatedness — "king" is to "queen" as "man" is to "woman," "Paris" sits near "France."
This sourceWord2vec: discrete tokens placed in a real vector space so semantic relatedness becomes geometric proximity, supporting analogy and nearest-neighbor operations.
- The guest is a discrete vocabulary of several hundred thousand tokens, whose native structure is semantic relatedness — "king" is to "queen" as "man" is to "woman," "Paris" sits near "France."
- Paradigmatic vs. Syntagmatic Relations
- Modern word-embeddings (Mikolov 2013,
This sourceMikolov word2vec word-embeddings distributional semantics paradigmatic proximity.
- Modern word-embeddings (Mikolov 2013,
- Signifier–Signified Duality
- Artificial Intelligence and Natural Language Processing (Mikolov 2013, Pennington et al. 2014): Modern word-embeddings (word2vec, GloVe) operationalize Saussure's insight about systemic-differential meaning: word meaning is captured as a vector in high-dimensional space, where meaning is relational position, not absolute property.
This sourceMikolov word2vec distributional semantics word-embeddings vector space.
- Artificial Intelligence and Natural Language Processing (Mikolov 2013, Pennington et al. 2014): Modern word-embeddings (word2vec, GloVe) operationalize Saussure's insight about systemic-differential meaning: word meaning is captured as a vector in high-dimensional space, where meaning is relational position, not absolute property.
- Vector Space
- Vector embeddings turned discrete objects — words, users, products — into vectors so that geometry encodes similarity, enabling clustering, recommendation, and analogy reasoning, with the classic king − man + woman ≈ queen exploiting vector-space structure directly.
This sourceWord embeddings where analogy is a displacement vector — king - man + woman ~ queen, and cosine similarity measures relatedness as an angle.
- Vector embeddings turned discrete objects — words, users, products — into vectors so that geometry encodes similarity, enabling clustering, recommendation, and analogy reasoning, with the classic king − man + woman ≈ queen exploiting vector-space structure directly.
Domain-specific¶
Verification¶
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Does it back the claim? Not recorded. Neither this nor any other of the 5 citations of this work carries a recorded support check.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 1 other way.
Registry ID ref:bf8026d0d6cf · see in the full table