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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 876–900 of 4002 papers

TitleStatusHype
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings—0
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding—0
Arabic aspect sentiment polarity classification using BERT—0
Language Models as Zero-shot Visual Semantic Learners—0
Stress Test Evaluation of Biomedical Word EmbeddingsCode0
Theoretical foundations and limits of word embeddings: what types of meaning can they capture?—0
Improved Text Classification via Contrastive Adversarial Training—0
Using Adversarial Debiasing to Remove Bias from Word Embeddings—0
Debiasing Multilingual Word Embeddings: A Case Study of Three Indian LanguagesCode0
Unsupervised Identification of Relevant Prior CasesCode0
Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF—0
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review—0
Sentence-level Online Handwritten Chinese Character Recognition—0
DUKweb: Diachronic word representations from the UK Web Archive corpusCode0
Tackling COVID-19 Infodemic using Deep Learning—0
Leveraging Domain Agnostic and Specific Knowledge for Acronym Disambiguation—0
Cross-lingual alignments of ELMo contextual embeddings—0
A Simple and Efficient Probabilistic Language model for Code-Mixed Text—0
Hate speech detection using static BERT embeddings—0
SAT Based Analogy Evaluation Framework for Persian Word Embeddings—0
A Source-Criticism Debiasing Method for GloVe EmbeddingsCode1
Multilingual transfer of acoustic word embeddings improves when training on languages related to the target zero-resource languageCode0
Clinical Named Entity Recognition using Contextualized Token Representations—0
Mixtures of Deep Neural Experts for Automated Speech Scoring—0
Membership Inference on Word Embedding and Beyond—0
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