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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 3481–3490 of 4002 papers

TitleStatusHype
Cooperative Self-training of Machine Reading Comprehension—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation—0
CopyBERT: A Unified Approach to Question Generation with Self-Attention—0
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach—0
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents—0
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors—0
Correlation Analysis of Chronic Obstructive Pulmonary Disease (COPD) and its Biomarkers Using the Word Embeddings—0
Count-Based and Predictive Language Models for Exploring DeReKo—0
Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models—0
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