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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 1326–1350 of 4002 papers

TitleStatusHype
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines—0
A Dual Embedding Space Model for Document Ranking—0
A Comparative Study of Transformers on Word Sense Disambiguation—0
Correlation Analysis of Chronic Obstructive Pulmonary Disease (COPD) and its Biomarkers Using the Word Embeddings—0
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors—0
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Analyzing Semantic Change in Japanese Loanwords—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Attention Modeling for Targeted Sentiment—0
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Attention improves concentration when learning node embeddings—0
Analyzing Correlations Between Intrinsic and Extrinsic Bias Metrics of Static Word Embeddings With Their Measuring Biases Aligned—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
Cooperative Self-training of Machine Reading Comprehension—0
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Attention-based Semantic Priming for Slot-filling—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings—0
Attending to Characters in Neural Sequence Labeling Models—0
Analyzing autoencoder-based acoustic word embeddings—0
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