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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 711–720 of 4002 papers

TitleStatusHype
An Unsupervised Approach for Mapping between Vector Spaces—0
CLaC at SMM4H 2020: Birth Defect Mention Detection—0
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM—0
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings—0
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words—0
A Review of Cross-Domain Text-to-SQL Models—0
Classification Attention for Chinese NER—0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Combining Word Embeddings and N-grams for Unsupervised Document Summarization—0
BLISS in Non-Isometric Embedding Spaces—0
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