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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 1171–1180 of 4002 papers

TitleStatusHype
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Distilled embedding: non-linear embedding factorization using knowledge distillation—0
Distilled Wasserstein Learning for Word Embedding and Topic Modeling—0
A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization—0
Distilling Word Embeddings: An Encoding Approach—0
Bilingually-constrained Phrase Embeddings for Machine Translation—0
Distinguishing Japanese Non-standard Usages from Standard Ones—0
Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible—0
Developing Conversational Data and Detection of Conversational Humor in Telugu—0
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