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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 2051–2100 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
Better OOV Translation with Bilingual Terminology Mining—0
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations—0
Visualising WordNet Embeddings: some preliminary results—0
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation ModelsCode0
Supervised Contextual Embeddings for Transfer Learning in Natural Language Processing TasksCode0
Classification and Clustering of Arguments with Contextualized Word EmbeddingsCode0
Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit SegmentationCode0
Variational Sequential Labelers for Semi-Supervised LearningCode0
Learning Bilingual Word Embeddings Using Lexical Definitions—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
An Open-World Extension to Knowledge Graph Completion ModelsCode0
Considerations for the Interpretation of Bias Measures of Word Embeddings—0
A Structured Distributional Model of Sentence Meaning and Processing—0
KaWAT: A Word Analogy Task Dataset for IndonesianCode0
Conceptor Debiasing of Word Representations Evaluated on WEAT—0
Character n-gram Embeddings to Improve RNN Language Models—0
Semantic Change and Semantic Stability: Variation is Key—0
Analyzing the Limitations of Cross-lingual Word Embedding Mappings—0
Putting words in context: LSTM language models and lexical ambiguityCode0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
A Systematic Comparison of English Noun Compound RepresentationsCode0
Word-level Speech Recognition with a Letter to Word Encoder—0
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media—0
Probing for Semantic Classes: Diagnosing the Meaning Content of Word EmbeddingsCode0
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations—0
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