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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 1571–1580 of 4002 papers

TitleStatusHype
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference—0
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
Extrapolating Binder Style Word Embeddings to New Words—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings—0
Extractive Summarization using Continuous Vector Space Models—0
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