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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 2051–2075 of 4002 papers

TitleStatusHype
Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization—0
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings—0
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic—0
Apprentissage de plongements lexicaux par une approche r\'eseaux complexes (Complex networks based word embeddings)—0
Apprentissage de plongements de mots dynamiques avec r\'egularisation de la d\'erive (Learning dynamic word embeddings with drift regularisation)—0
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode DecompositionCode0
Embedding Strategies for Specialized Domains: Application to Clinical Entity RecognitionCode0
Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance—0
Exploring Numeracy in Word Embeddings—0
Word2Sense: Sparse Interpretable Word Embeddings—0
Few-Shot Representation Learning for Out-Of-Vocabulary WordsCode0
Fitting Semantic Relations to Word Embeddings—0
Assessing Wordnets with WordNet EmbeddingsCode0
Hybridation d'un agent conversationnel avec des plongements lexicaux pour la formation au diagnostic m\'edical (Hybridization of a conversational agent with word embeddings for medical diagnostic training)—0
Collocation Classification with Unsupervised Relation VectorsCode0
Self-Attention Architectures for Answer-Agnostic Neural Question Generation—0
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings—0
Unsupervised Joint Training of Bilingual Word Embeddings—0
Robust to Noise Models in Natural Language Processing TasksCode0
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine TranslationCode0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Reliability-aware Dynamic Feature Composition for Name TaggingCode0
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories—0
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation EvaluationCode0
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference LearningCode0
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