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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 2651–2660 of 4002 papers

TitleStatusHype
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy—0
Learning and Evaluating Character Representations in Novels—0
Learning and Evaluating Sparse Interpretable Sentence Embeddings—0
Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network—0
Learning a POS tagger for AAVE-like language—0
Learning Articulated Motion Models from Visual and Lingual Signals—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision—0
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification—0
Learning Bilingual Word Embeddings Using Lexical Definitions—0
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