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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 1741–1750 of 4002 papers

TitleStatusHype
Robust Cross-lingual Embeddings from Parallel SentencesCode0
Encoding word order in complex embeddingsCode0
Job Prediction: From Deep Neural Network Models to Applications—0
One-Shot Weakly Supervised Video Object Segmentation—0
Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word EmbeddingsCode0
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task—0
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models—0
Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting—0
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