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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 2931–2940 of 4002 papers

TitleStatusHype
Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features—0
Multilingual Culture-Independent Word Analogy Datasets—0
Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba—0
Multi-lingual Entity Discovery and Linking—0
Multilingual Learning for Mild Cognitive Impairment Screening from a Clinical Speech Task—0
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features—0
Multilingual Model Using Cross-Task Embedding Projection—0
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations—0
Multilingual Offensive Language Identification for Low-resource Languages—0
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping—0
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