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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 3831–3840 of 4002 papers

TitleStatusHype
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
Evaluating Sub-word Embeddings in Cross-lingual Models—0
Evaluating the Consistency of Word Embeddings from Small Data—0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling—0
Evaluating the Stability of Embedding-based Word Similarities—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings—0
Evaluating vector-space models of analogy—0
Evaluating Word Embedding Hyper-Parameters for Similarity and Analogy Tasks—0
Evaluating Word Embedding Models: Methods and Experimental Results—0
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