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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 1711–1720 of 4002 papers

TitleStatusHype
Semantic Relatedness and Taxonomic Word Embeddings—0
Word Embeddings Inherently Recover the Conceptual Organization of the Human Mind—0
Fast and Robust Comparison of Probability Measures in Heterogeneous SpacesCode0
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings—0
Detecting Fake News with Capsule Neural Networks—0
Pretrained Transformers for Simple Question Answering over Knowledge GraphsCode0
A Deep Neural Framework for Contextual Affect Detection—0
Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook—0
Bias in word embeddings—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
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