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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
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—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
Detecting Cross-Lingual Plagiarism Using Simulated Word Embeddings—0
Fine-tuning BERT to classify COVID19 tweets containing symptoms—0
BERT's Conceptual Cartography: Mapping the Landscapes of Meaning—0
Affordance Extraction and Inference based on Semantic Role Labeling—0
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