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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 1626–1650 of 4002 papers

TitleStatusHype
Topological Data Analysis in Text Classification: Extracting Features with Additive Information—0
Cycle Text-To-Image GAN with BERTCode1
Machine learning as a model for cultural learning: Teaching an algorithm what it means to be fatCode1
Data-driven models and computational tools for neurolinguistics: a language technology perspectiveCode0
Temporal Embeddings and Transformer Models for Narrative Text Understanding—0
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá—0
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic—0
Leveraging Foreign Language Labeled Data for Aspect-Based Opinion Mining—0
Word Sense Disambiguation for 158 Languages using Word Embeddings Only—0
Text Similarity Using Word Embeddings to Classify Misinformation—0
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at ScaleCode1
Using word embeddings to improve the discriminability of co-occurrence text networks—0
A Graph Convolutional Topic Model for Short and Noisy Text StreamsCode1
Hurtful Words: Quantifying Biases in Clinical Contextual Word EmbeddingsCode1
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis—0
Text classification with word embedding regularization and soft similarity measureCode0
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity—0
Joint Multiclass Debiasing of Word EmbeddingsCode0
Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages—0
Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes—0
Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network—0
Quality of Word Embeddings on Sentiment Analysis Tasks—0
Captioning Images with Novel Objects via Online Vocabulary Expansion—0
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
Transformer++—0
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