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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 2076–2100 of 4002 papers

TitleStatusHype
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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