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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 2721–2730 of 4002 papers

TitleStatusHype
Learning to Generate Word Representations using Subword Information—0
Learning to Lemmatize in the Word Representation Space—0
Learning to Name Classes for Vision and Language Models—0
Learning to Negate Adjectives with Bilinear Models—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings—0
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks—0
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
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