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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 1501–1525 of 4002 papers

TitleStatusHype
Estimating Mutual Information Between Dense Word Embeddings—0
Character aware models with similarity learning for metaphor detection—0
Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies—0
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction—0
LIT Team's System Description for Japanese-Chinese Machine Translation Task in IWSLT 2020—0
He said ``who's gonna take care of your children when you are at ACL?'': Reported Sexist Acts are Not Sexist—0
Metaphor Detection Using Contextual Word Embeddings From Transformers—0
Transition-based Semantic Dependency Parsing with Pointer Networks—0
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings—0
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings—0
Predicting Degrees of Technicality in Automatic Terminology Extraction—0
Neural-DINF: A Neural Network based Framework for Measuring Document Influence—0
Supervised Understanding of Word Embeddings—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations—0
Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy—0
MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline—0
On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms—0
Evaluating a Multi-sense Definition Generation Model for Multiple Languages—0
Attention improves concentration when learning node embeddings—0
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysisCode0
Combining word embeddings and convolutional neural networks to detect duplicated questions—0
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