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Hyperbolic Centroid Calculations for Text Classification

arXiv 2211.04462 ↗First posted 8 Nov 2022cs.CLcited by 0

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Abstract (arXiv)

A new development in NLP is the construction of hyperbolic word embeddings. As opposed to their Euclidean counterparts, hyperbolic embeddings are represented not by vectors, but by points in hyperbolic space. This makes the most common basic scheme for constructing document representations, namely the averaging of word vectors, meaningless in the hyperbolic setting. We reinterpret the vector mean as the centroid of the points represented by the vectors, and investigate various hyperbolic centroid schemes and their effectiveness at text classification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 8 Nov 2022.

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References · 19

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7 more
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