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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 2951–2960 of 4002 papers

TitleStatusHype
Multimodal Frame Identification with Multilingual Evaluation—0
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data—0
Multimodal Representation Loss Between Timed Text and Audio for Regularized Speech Separation—0
Multi-Modal Representations for Improved Bilingual Lexicon Learning—0
Multimodal Semantic Learning from Child-Directed Input—0
Multi-Model and Crosslingual Dependency Analysis—0
Multi-Module Recurrent Neural Networks with Transfer Learning—0
Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Multi-sense Definition Modeling using Word Sense Decompositions—0
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