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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 3211–3220 of 4002 papers

TitleStatusHype
Skill2vec: Machine Learning Approach for Determining the Relevant Skills from Job DescriptionCode0
Analysis of Italian Word Embeddings—0
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Reconstruction of Word Embeddings from Sub-Word Parameters—0
Mimicking Word Embeddings using Subword RNNsCode0
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
Toward Incorporation of Relevant Documents in word2vec—0
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