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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 1226–1250 of 4002 papers

TitleStatusHype
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Introducing Orthogonal Constraint in Structural ProbesCode0
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings—0
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali LanguageCode0
WEmbSim: A Simple yet Effective Metric for Image Captioning—0
Improved Biomedical Word Embeddings in the Transformer EraCode0
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings—0
Intrinsic Image Captioning Evaluation—0
A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings—0
Discriminative Pre-training for Low Resource Title Compression in Conversational Grocery—0
TF-CR: Weighting Embeddings for Text ClassificationCode0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
Cross-lingual Word Sense Disambiguation using mBERT Embeddings with Syntactic Dependencies—0
A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings—0
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity SearchCode0
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic ChangeCode0
A Computational Approach to Measuring the Semantic Divergence of Cognates—0
Automatic Word Association Norms (AWAN)—0
“Shakespeare in the Vectorian Age” – An evaluation of different word embeddings and NLP parameters for the detection of Shakespeare quotes—0
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings—0
Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation TaskCode0
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings—0
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora—0
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