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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 1241–1250 of 4002 papers

TitleStatusHype
An Automated Method to Enrich Consumer Health Vocabularies Using GloVe Word Embeddings and An Auxiliary Lexical Resource—0
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Cseq2seq: Cyclic Sequence-to-Sequence Learning—0
Automated Trustworthiness Testing for Machine Learning Classifiers—0
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation—0
CroVeWA: Crosslingual Vector-Based Writing Assistance—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Crossword: Estimating Unknown Embeddings using Cross Attention and Alignment Strategies—0
Automated Single-Label Patent Classification using Ensemble Classifiers—0
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