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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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TitleStatusHype
Definition Frames: Using Definitions for Hybrid Concept RepresentationsCode0
Combining Representations For Effective Citation ClassificationCode0
A Simple Approach to Learn Polysemous Word EmbeddingsCode0
Definition Modeling: Learning to define word embeddings in natural languageCode0
Density Matching for Bilingual Word EmbeddingCode0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
Language Models with Pre-Trained (GloVe) Word EmbeddingsCode0
Bidirectional LSTM-CRF for Clinical Concept ExtractionCode0
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation ExtractionCode0
Learning Composition Models for Phrase EmbeddingsCode0
Deep word embeddings for visual speech recognitionCode0
Learning Diachronic Analogies to Analyze Concept ChangeCode0
Deep Text Mining of Instagram Data Without Strong SupervisionCode0
Common Sense Bias in Semantic Role LabelingCode0
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal AdaptationCode0
Learning language through picturesCode0
Comparative Analysis of Word Embeddings for Capturing Word SimilaritiesCode0
Bidirectional Attention as a Mixture of Continuous Word ExpertsCode0
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
Learning Neural Word Salience ScoresCode0
Learning Personal Food Preferences via Food Logs EmbeddingCode0
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and DocumentsCode0
Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRFCode0
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal GuidanceCode0
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