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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 1371–1380 of 4002 papers

TitleStatusHype
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
Context-aware Feature Generation for Zero-shot Semantic SegmentationCode1
MICE: Mining Idioms with Contextual EmbeddingsCode0
Context Reinforced Neural Topic Modeling over Short TextsCode0
A Neural Generative Model for Joint Learning Topics and Topic-Specific Word EmbeddingsCode0
Discovering and Categorising Language Biases in RedditCode1
An exploration of the encoding of grammatical gender in word embeddings—0
Deep Learning based Topic Analysis on Financial Emerging Event Tweets—0
Combining Representations For Effective Citation ClassificationCode0
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