May 14, 2024, 4:50 a.m. | Karahan Sar{\i}ta\c{s}, Cahid Arda \"Oz, Tunga G\"ung\"or

cs.CL updates on

arXiv:2405.07778v1 Announce Type: new
Abstract: Word embeddings are fixed-length, dense and distributed word representations that are used in natural language processing (NLP) applications. There are basically two types of word embedding models which are non-contextual (static) models and contextual models. The former method generates a single embedding for a word regardless of its context, while the latter method produces distinct embeddings for a word based on the specific contexts in which it appears. There are plenty of works that compare …

abstract analysis applications arxiv distributed embedding embedding models embeddings language language processing natural natural language natural language processing nlp processing type types word word embedding word embeddings

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