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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 2631–2640 of 4002 papers

TitleStatusHype
Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting—0
Language Transfer Learning for Supervised Lexical Substitution—0
Large scale analysis of gender bias and sexism in song lyrics—0
Large Scale Substitution-based Word Sense Induction—0
Large-scale Taxonomy Induction Using Entity and Word Embeddings—0
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification—0
LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model—0
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings—0
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention—0
Latent Topic Embedding—0
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