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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 1671–1680 of 4002 papers

TitleStatusHype
Detecting Fake News with Capsule Neural Networks—0
Pretrained Transformers for Simple Question Answering over Knowledge GraphsCode0
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
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
The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word EmbeddingsCode1
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
A Deep Learning Approach to Behavior-Based Learner Modeling—0
Zero-Shot Activity Recognition with Videos—0
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