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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 2026–2050 of 4002 papers

TitleStatusHype
Self-Attention Architectures for Answer-Agnostic Neural Question Generation—0
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View—0
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference LearningCode0
Collocation Classification with Unsupervised Relation VectorsCode0
De-Mixing Sentiment from Code-Mixed Text—0
Word2Sense: Sparse Interpretable Word Embeddings—0
Unsupervised Joint Training of Bilingual Word Embeddings—0
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation EvaluationCode0
Embedding Strategies for Specialized Domains: Application to Clinical Entity RecognitionCode0
Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization—0
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language ModelsCode1
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic—0
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation ModelsCode0
Robust to Noise Models in Natural Language Processing TasksCode0
Reliability-aware Dynamic Feature Composition for Name TaggingCode0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories—0
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting—0
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings—0
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine TranslationCode0
Few-Shot Representation Learning for Out-Of-Vocabulary WordsCode0
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing TasksCode0
Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit SegmentationCode0
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