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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 3061–3070 of 4002 papers

TitleStatusHype
One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words—0
One-shot and few-shot learning of word embeddings—0
One-shot and few-shot learning of word embeddings—0
One-Shot Weakly Supervised Video Object Segmentation—0
On Evaluation of Bangla Word Analogies—0
On Initializing Transformers with Pre-trained Embeddings—0
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data—0
On Learning Word Embeddings From Linguistically Augmented Text Corpora—0
Online Fake Review Detection Using Supervised Machine Learning And BERT Model—0
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm—0
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