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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 1001–1025 of 4002 papers

TitleStatusHype
Eliciting Explicit Knowledge From Domain Experts in Direct Intrinsic Evaluation of Word Embeddings for Specialized DomainsCode0
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis—0
Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models—0
Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism—0
Cross-Lingual Transfer Learning for Hate Speech Detection—0
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings—0
Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain—0
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding—0
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
Handling Out-Of-Vocabulary Problem in Hangeul Word Embeddings—0
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter AccessCode1
Evaluating Neural Word Embeddings for SanskritCode0
Self-Supervised Euphemism Detection and Identification for Content ModerationCode1
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey—0
Locally-Contextual Nonlinear CRFs for Sequence Labeling—0
Probabilistic Analogical Mapping with Semantic Relation Networks—0
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications—0
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP ModelsCode1
An Introduction to Robust Graph Convolutional Networks—0
When Word Embeddings Become Endangered—0
SparseGAN: Sparse Generative Adversarial Network for Text Generation—0
Monolingual and Parallel Corpora for Kangri Low Resource LanguageCode0
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus—0
Attention-based model for predicting question relatedness on Stack Overflow—0
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