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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 2541–2550 of 4002 papers

TitleStatusHype
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models—0
Understanding the Stability of Medical Concept Embeddings—0
Understanding the Source of Semantic Regularities in Word Embeddings—0
Understanding Undesirable Word Embedding Associations—0
UnibucKernel: A kernel-based learning method for complex word identification—0
Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification—0
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