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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 2611–2620 of 4002 papers

TitleStatusHype
Code-Switched Named Entity Recognition with Embedding Attention—0
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimizationCode0
Comparison of Representations of Named Entities for Document Classification—0
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Connecting Supervised and Unsupervised Sentence Embeddings—0
Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline—0
Word Embeddings-Based Uncertainty Detection in Financial Disclosures—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
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