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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 3961–3970 of 4002 papers

TitleStatusHype
Finding Individual Word Sense Changes and their Delay in Appearance—0
Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task—0
Fine-Grained Contextual Predictions for Hard Sentiment Words—0
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis—0
Firearms and Tigers are Dangerous, Kitchen Knives and Zebras are Not: Testing whether Word Embeddings Can Tell—0
First Bilingual Word Embeddings for te reo Māori and English: Towards Code-switching Detection in a Low-resourced setting—0
Fitting Semantic Relations to Word Embeddings—0
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