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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 2751–2760 of 4002 papers

TitleStatusHype
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks—0
Leveraging a Bilingual Dictionary to Learn Wolastoqey Word Representations—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Leveraging Contextual Embeddings and Idiom Principle for Detecting Idiomaticity in Potentially Idiomatic Expressions—0
Leveraging distributed representations and lexico-syntactic fixedness for token-level prediction of the idiomaticity of English verb-noun combinations—0
Leveraging Distributional Semantics for Multi-Label Learning—0
Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation—0
Leveraging English Word Embeddings for Semi-Automatic Semantic Classification in Nêhiyawêwin (Plains Cree)—0
Leveraging Foreign Language Labeled Data for Aspect-Based Opinion Mining—0
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation—0
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