Research Topic · Peer-Reviewed

Word

embedding Word embedding is a method used in the natural language processing field, which maps words in a text to points in a multi-dimensional vector space. The aim of this mapping is to represent the semantic relationships between words and phrases of the text. This allows computers to better understand the meani…

Curated from this journal's research 📚 12 peer-reviewed articles cited Cited 19× across the literature 🔖 ISSN 2998-4122 🗓 Reviewed June 2026

Overview

embedding Word embedding is a method used in the natural language processing field, which maps words in a text to points in a multi-dimensional vector space. The aim of this mapping is to represent the semantic relationships between words and phrases of the text. This allows computers to better understand the meanings of sentences, words, and phrases in a quantitative approach. Word embeddings are used in many natural language processing tasks such as sentiment analysis, machine translation, and text summarization. They can also be used for various tasks such as text classification, topic modelling, and document clustering. This technology enables computers to better understand language, which has a wide range of applications in the fields of artificial intelligence, natural language processing and machine learning.

Research published in this journal

12 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 12 articles above have been cited 19 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Word, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Language Research (ISSN 2998-4122).

Journal editorial board
Marcel Pikhart · Czech Republic Óscar Navarro · Spain

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