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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 3511–3520 of 4002 papers

TitleStatusHype
Encoding word order in complex embeddingsCode0
Learning Eligibility in Cancer Clinical Trials using Deep Neural NetworksCode0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-levelCode0
End-to-End Text Classification via Image-based Embedding using Character-level NetworksCode0
On Measuring Social Biases in Sentence EncodersCode0
Variational Sequential Labelers for Semi-Supervised LearningCode0
Semantics-aware BERT for Language UnderstandingCode0
Alternative Weighting Schemes for ELMo EmbeddingsCode0
Semantic Sensitive TF-IDF to Determine Word Relevance in DocumentsCode0
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