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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 821–830 of 4002 papers

TitleStatusHype
A Hmong Corpus with Elaborate Expression Annotations—0
Compositional Fusion of Signals in Data Embedding—0
Compositional Morpheme Embeddings with Affixes as Functions and Stems as Arguments—0
Compound Embedding Features for Semi-supervised Learning—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable—0
Compressing Word Embeddings—0
Compressing Word Embeddings Using Syllables—0
An RNN-based Binary Classifier for the Story Cloze Test—0
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