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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 3626–3650 of 4002 papers

TitleStatusHype
Actionable and Political Text Classification using Word Embeddings and LSTM—0
Representation learning for very short texts using weighted word embedding aggregationCode0
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic ResourceCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Exact gradient updates in time independent of output size for the spherical loss family—0
Using Word Embeddings for Automatic Query Expansion—0
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation—0
Toward Word Embedding for Personalized Information Retrieval—0
Using Word Embeddings in Twitter Election Classification—0
An empirical study on large scale text classification with skip-gram embeddings—0
Neural Morphological Tagging from Characters for Morphologically Rich Languages—0
Quantifying and Reducing Stereotypes in Word Embeddings—0
Siamese CBOW: Optimizing Word Embeddings for Sentence RepresentationsCode0
Learning Word Sense Embeddings from Word Sense Definitions—0
Active Discriminative Text Representation Learning—0
PSDVec: a Toolbox for Incremental and Scalable Word Embedding—0
Inducing Domain-Specific Sentiment Lexicons from Unlabeled CorporaCode0
Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays—0
A Joint Model for Word Embedding and Word Morphology—0
Multilingual Visual Sentiment Concept Matching—0
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies—0
Learning a POS tagger for AAVE-like language—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
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