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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 3021–3030 of 4002 papers

TitleStatusHype
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings—0
An empirical study on large scale text classification with skip-gram embeddings—0
An Empirical Study on Post-processing Methods for Word Embeddings—0
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding—0
An Empirical Study on the Fairness of Pre-trained Word Embeddings—0
An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets—0
A Neural Local Coherence Model for Text Quality Assessment—0
A Neural Model for Compositional Word Embeddings and Sentence Processing—0
A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models—0
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