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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 2901–2925 of 4002 papers

TitleStatusHype
Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification—0
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation—0
Models in the Wild: On Corruption Robustness of NLP Systems—0
Modifications of Machine Translation Evaluation Metrics by Using Word Embeddings—0
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.—0
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics—0
More Embeddings, Better Sequence Labelers?—0
More Romanian word embeddings from the RETEROM project—0
Morphological Disambiguation of South S\'ami with FSTs and Neural Networks—0
Morphological Disambiguation of South Sámi with FSTs and Neural Networks—0
Morphological Priors for Probabilistic Neural Word Embeddings—0
Morphological Skip-Gram: Using morphological knowledge to improve word representation—0
Morphological Smoothing and Extrapolation of Word Embeddings—0
Morphological Word-Embeddings—0
Morphological Word Embeddings—0
Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings—0
Morphology-rich Alphasyllabary Embeddings—0
MORTY Embedding: Improved Embeddings without Supervision—0
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer—0
Moving TIGER beyond Sentence-Level—0
Multi-Adversarial Learning for Cross-Lingual Word Embeddings—0
Multi-Attention Network for One Shot Learning—0
Multi-Cast Attention Networks for Retrieval-based Question Answering and Response Prediction—0
Multichannel Variable-Size Convolution for Sentence Classification—0
Multi-class Regret Detection in Hindi Devanagari Script—0
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