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From ContE to Entity Type Embeddings in Natural Language Processing
Latest   Machine Learning

From ContE to Entity Type Embeddings in Natural Language Processing

Last Updated on July 24, 2023 by Editorial Team

Author(s): Edward Ma

Originally published on Towards AI.


Photo by twinsfisch on Unsplash

In the previous story, TransE (Border et al., 2013) use a translating mechanism to transform subject (s) by the predicate (p) to object (o). Just like word2vec, we can compute β€œQueen” by using β€œKing” + β€œMan” embeddings.

Word2vec Sample: King + Woman ~= Queen (source)

Moon et al. (2017) consider contextual relation to solving graph completion. Authors consider not only the triplet (s, p, o) but also outgoing relation types (from s) and incoming relation types (to o).

Contextual relation for a triple (s, p, o). (Moon et al., 2017)

When two entities (β€œAlex Guinness” and β€œStar Wars”) have relations… Read the full blog for free on Medium.

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