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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 1981–1990 of 4002 papers

TitleStatusHype
Japanese Lexical Simplification for Non-Native Speakers—0
Japanese Word Readability Assessment using Word Embeddings—0
Evaluation of Morphological Embeddings for the Russian Language—0
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena—0
Evaluation of Greek Word Embeddings—0
JHU System Description for the MADAR Arabic Dialect Identification Shared Task—0
Classification of Micro-Texts Using Sub-Word Embeddings—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
A Locally Linear Procedure for Word Translation—0
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