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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 3091–3100 of 4002 papers

TitleStatusHype
Word Re-Embedding via Manifold Dimensionality Retention—0
Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction—0
Sentiment Lexicon Construction with Representation Learning Based on Hierarchical Sentiment SupervisionCode0
Towards a Universal Sentiment Classifier in Multiple languages—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model—0
Word-Context Character Embeddings for Chinese Word Segmentation—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Predicting Word Association Strengths—0
Distinguishing Japanese Non-standard Usages from Standard Ones—0
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