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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 2411–2420 of 4002 papers

TitleStatusHype
The Limits of Word Level Differential Privacy—0
The LMU Munich Unsupervised Machine Translation Systems—0
The LMU Munich Unsupervised Machine Translation System for WMT19—0
The Making of the Royal Society Corpus—0
Theoretical foundations and limits of word embeddings: what types of meaning can they capture?—0
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task—0
The Role of Context Types and Dimensionality in Learning Word Embeddings—0
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations—0
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018—0
The SAME score: Improved cosine based bias score for word embeddings—0
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