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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 3451–3475 of 4002 papers

TitleStatusHype
A Systematic Comparison of English Noun Compound RepresentationsCode0
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse TargetsCode0
Learning and Evaluating Character Representations in NovelsCode0
Object Priors for Classifying and Localizing Unseen ActionsCode0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data SetCode0
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error StatesCode0
Efficient Vector Representation for Documents through CorruptionCode0
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition AnalysisCode0
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic ChangeCode0
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode DecompositionCode0
Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized DomainsCode0
Global Textual Relation Embedding for Relational UnderstandingCode0
Asynchronous Training of Word Embeddings for Large Text CorporaCode0
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image ClassifiersCode0
Unsupervised Open Relation ExtractionCode0
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine TranslationCode0
Interpretable Segmentation of Medical Free-Text Records Based on Word EmbeddingsCode0
Clustering-Based Article Identification in Historical NewspapersCode0
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme ClassificationCode0
Training Cross-Lingual embeddings for Setswana and SepediCode0
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual LearningCode0
Embeddings Evaluation Using a Novel Measure of Semantic SimilarityCode0
Training Temporal Word Embeddings with a CompassCode0
Embeddings for Word Sense Disambiguation: An Evaluation StudyCode0
On Dimensional Linguistic Properties of the Word Embedding SpaceCode0
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