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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 2831–2840 of 4002 papers

TitleStatusHype
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research—0
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings—0
Machine Translation for Accessible Multi-Language Text Analysis—0
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information—0
Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs—0
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment—0
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