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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 661–670 of 4002 papers

TitleStatusHype
A Comparative Study on Word Embeddings and Social NLP TasksCode0
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding—0
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?—0
Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification—0
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings—0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
TurkishDelightNLP: A Neural Turkish NLP ToolkitCode0
Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings—0
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and DefinitionsCode0
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