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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 2671–2680 of 4002 papers

TitleStatusHype
Learning Cross-lingual Representations with Matrix Factorization—0
Learning Cross-lingual Word Embeddings via Matrix Co-factorization—0
Learning Dictionaries for Named Entity Recognition using Minimal Supervision—0
Learning Distributed Representations for Multilingual Text Sequences—0
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings—0
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts—0
Learning dynamic word embeddings with drift regularisation—0
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding—0
Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships—0
Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization—0
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