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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 531–540 of 4002 papers

TitleStatusHype
Bio-inspired Structure Identification in Language Embeddings—0
Are Word Embedding-based Features Useful for Sarcasm Detection?—0
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction—0
Adapting word2vec to Named Entity Recognition—0
A Review on Deep Learning Techniques Applied to Answer Selection—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
A Locally Linear Procedure for Word Translation—0
BioAMA: Towards an End to End BioMedical Question Answering System—0
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing—0
All-words Word Sense Disambiguation Using Concept Embeddings—0
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