Detailed Information on Publication Record
2021
Precomputed Word Embeddings for 15+ Languages
HERMAN, OndřejBasic information
Original name
Precomputed Word Embeddings for 15+ Languages
Authors
HERMAN, Ondřej (203 Czech Republic, guarantor, belonging to the institution)
Edition
Brno, Recent Advances in Slavonic Natural Language Processing (RASLAN 2021), p. 41-46, 6 pp. 2021
Publisher
Tribun EU
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10200 1.2 Computer and information sciences
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
References:
RIV identification code
RIV/00216224:14330/21:00123246
Organization unit
Faculty of Informatics
ISBN
978-80-263-1670-1
ISSN
Keywords in English
Word embeddings; Sketch Engine; Corpora
Změněno: 15/5/2024 02:13, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
Word embeddings serve as an useful resource for many downstream natural language processing tasks. The embeddings map or embed the lexicon of a language onto a vector space, in which various operations can be carried out easily using the established machinery of linear algebra. The unbounded nature of the language can be problematic and word embeddings provide a way of compressing the words into a manageable dense space. The position of a word in the vector space is given by the context the word appears in, or, as the distributional hypothesis postulates, a word is characterized by the company it keeps [2]. As similar words appear in similar contexts, their positions will also be close to each other in the embedding vector space. Because of this many useful semantical properties of words are preserved in the embedding vector space.
Links
| LM2018101, research and development project |
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