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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 1441–1450 of 4002 papers

TitleStatusHype
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
300-sparsans at SemEval-2018 Task 9: Hypernymy as interaction of sparse attributes—0
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning—0
Compressing Word Embeddings Using Syllables—0
A Structured Distributional Model of Sentence Meaning and Processing—0
Compressing Word Embeddings—0
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable—0
A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains—0
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