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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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TitleStatusHype
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Learning Word Representations with Regularization from Prior Knowledge—0
Learning Word Sense Embeddings from Word Sense Definitions—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
Learnt Contrastive Concept Embeddings for Sign Recognition—0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
Lessons in Reproducibility: Insights from NLP Studies in Materials Science—0
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet—0
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