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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 751–775 of 4002 papers

TitleStatusHype
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment AnalysisCode0
Degree-Aware Alignment for Entities in TailCode0
Density Matching for Bilingual Word EmbeddingCode0
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor DetectionCode0
Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist NewspapersCode0
DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entriesCode0
A Self-supervised Representation Learning of Sentence Structure for Authorship AttributionCode0
Incorporating Subword Information into Matrix Factorization Word EmbeddingsCode0
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and DocumentsCode0
Inducing Domain-Specific Sentiment Lexicons from Unlabeled CorporaCode0
Diachronic Word Embeddings Reveal Statistical Laws of Semantic ChangeCode0
Collapsed Language Models Promote FairnessCode0
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective DetectionCode0
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short TextsCode0
Inorganic Materials Synthesis Planning with Literature-Trained Neural NetworksCode0
Dynamic Bernoulli Embeddings for Language EvolutionCode0
Enhancing Word Embeddings with Knowledge Extracted from Lexical ResourcesCode0
Deep Text Mining of Instagram Data Without Strong SupervisionCode0
Introducing Orthogonal Constraint in Structural ProbesCode0
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic ChangesCode0
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationCode0
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal GuidanceCode0
Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit SegmentationCode0
Deep word embeddings for visual speech recognitionCode0
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