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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 1421–1430 of 4002 papers

TitleStatusHype
Neural-DINF: A Neural Network based Framework for Measuring Document Influence—0
In Neural Machine Translation, What Does Transfer Learning Transfer?—0
Adaptive Compression of Word Embeddings—0
Estimating Mutual Information Between Dense Word Embeddings—0
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
He said ``who's gonna take care of your children when you are at ACL?'': Reported Sexist Acts are Not Sexist—0
Transition-based Semantic Dependency Parsing with Pointer Networks—0
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
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