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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 3081–3090 of 4002 papers

TitleStatusHype
Apprentissage de plongements de mots dynamiques avec r\'egularisation de la d\'erive (Learning dynamic word embeddings with drift regularisation)—0
Apprentissage de plongements de mots sur des corpus en langue de sp\'ecialit\'e : une \'etude d'impact (Learning word embeddings on domain specific corpora : an impact study )—0
Apprentissage de plongements lexicaux par une approche r\'eseaux complexes (Complex networks based word embeddings)—0
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis—0
A Preliminary Study on a Conceptual Game Feature Generation and Recommendation System—0
A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work—0
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
A Probabilistic Model for Learning Multi-Prototype Word Embeddings—0
A Process for Topic Modelling Via Word Embeddings—0
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