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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 3351–3360 of 4002 papers

TitleStatusHype
Classification of Micro-Texts Using Sub-Word Embeddings—0
Classifying Out-of-vocabulary Terms in a Domain-Specific Social Media Corpus—0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention—0
Classifying Text-Based Conspiracy Tweets related to COVID-19 using Contextualized Word Embeddings—0
CLCL (Geneva) DINN Parser: a Neural Network Dependency Parser Ten Years Later—0
CLFD: A Novel Vectorization Technique and Its Application in Fake News Detection—0
Clickbait detection using word embeddings—0
Clinical Abbreviation Disambiguation Using Neural Word Embeddings—0
Clinical Event Detection with Hybrid Neural Architecture—0
Clinical Named Entity Recognition using Contextualized Token Representations—0
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