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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 3626–3650 of 4002 papers

TitleStatusHype
Development of a Japanese Personality Dictionary based on Psychological Methods—0
Development of Word Embeddings for Uzbek Language—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
D-GloVe: A Feasible Least Squares Model for Estimating Word Embedding Densities—0
D-Graph: AI-Assisted Design Concept Exploration Graph—0
Diachronic degradation of language models: Insights from social media—0
Diachronic Embeddings for People in the News—0
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View—0
Diachronic word embeddings and semantic shifts: a survey—0
Dialectograms: Machine Learning Differences between Discursive Communities—0
Dialects Identification of Armenian Language—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network—0
Dialogue Session Segmentation by Embedding-Enhanced TextTiling—0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings—0
DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features—0
Different Contexts Lead to Different Word Embeddings—0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder—0
Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection—0
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings—0
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