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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 811–820 of 4002 papers

TitleStatusHype
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
Complementary Strategies for Low Resourced Morphological Modeling—0
Complex networks based word embeddings—0
Complex Ontology Matching with Large Language Model Embeddings—0
A Hmong Corpus with Elaborate Expression Annotations—0
Component-Enhanced Chinese Character Embeddings—0
Composing Knowledge Graph Embeddings via Word Embeddings—0
Composing Noun Phrase Vector Representations—0
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings—0
An RNN-based Binary Classifier for the Story Cloze Test—0
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