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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 2531–2540 of 4002 papers

TitleStatusHype
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts—0
Interpretable embeddings to understand computing careers—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
Interpretable Word Embedding Contextualization—0
Interpretable Word Embeddings via Informative Priors—0
Interpreting Emoji with Emoji—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Inter-Sense: An Investigation of Sensory Blending in Fiction—0
Inter-Weighted Alignment Network for Sentence Pair Modeling—0
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