Matches in SemOpenAlex for { <https://semopenalex.org/work/W79006051> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W79006051 endingPage "72" @default.
- W79006051 startingPage "62" @default.
- W79006051 abstract "Imbalanced training data poses a serious problem for supervised learning based text classification. Such a problem becomes more serious in emotion classification task with multiple emotion categories as the training data can be quite skewed. This paper presents a novel over-sampling method to form additional sum sentence vectors for minority classes in order to improve emotion classification for imbalanced data. Firstly, a large corpus is used to train a continuous skip-gram model to form each word vector using word/POS pair as the unit of word vector. The sentence vectors of the training data are then constructed as the sum vector of their word/POS vectors. The new minority class training samples are then generated by randomly add two sentence vectors in the corresponding class until the training samples for each class are the same so that the classifiers can be trained on fully balanced training dataset. Evaluations on NLP&CC2013 Chinese micro blog emotion classification dataset shows that the obtained classifier achieves 48.4% average precision, an 11.9 percent improvement over the state-of-art performance on this dataset (at 36.5%). This result shows that the proposed over-sampling method can effectively address the problem of data imbalance and thus achieve much improved performance for emotion classification." @default.
- W79006051 created "2016-06-24" @default.
- W79006051 creator A5000732066 @default.
- W79006051 creator A5016391596 @default.
- W79006051 creator A5018149714 @default.
- W79006051 creator A5020766468 @default.
- W79006051 creator A5024818901 @default.
- W79006051 creator A5048826252 @default.
- W79006051 creator A5069054353 @default.
- W79006051 date "2014-01-01" @default.
- W79006051 modified "2023-10-18" @default.
- W79006051 title "A Sentence Vector Based Over-Sampling Method for Imbalanced Emotion Classification" @default.
- W79006051 cites W180232814 @default.
- W79006051 cites W1966849089 @default.
- W79006051 cites W1989145535 @default.
- W79006051 cites W2053724458 @default.
- W79006051 cites W2099550922 @default.
- W79006051 cites W2119191234 @default.
- W79006051 cites W2147152072 @default.
- W79006051 cites W2155328222 @default.
- W79006051 cites W2166706824 @default.
- W79006051 doi "https://doi.org/10.1007/978-3-642-54903-8_6" @default.
- W79006051 hasPublicationYear "2014" @default.
- W79006051 type Work @default.
- W79006051 sameAs 79006051 @default.
- W79006051 citedByCount "8" @default.
- W79006051 countsByYear W790060512015 @default.
- W79006051 countsByYear W790060512016 @default.
- W79006051 countsByYear W790060512018 @default.
- W79006051 countsByYear W790060512019 @default.
- W79006051 countsByYear W790060512020 @default.
- W79006051 crossrefType "book-chapter" @default.
- W79006051 hasAuthorship W79006051A5000732066 @default.
- W79006051 hasAuthorship W79006051A5016391596 @default.
- W79006051 hasAuthorship W79006051A5018149714 @default.
- W79006051 hasAuthorship W79006051A5020766468 @default.
- W79006051 hasAuthorship W79006051A5024818901 @default.
- W79006051 hasAuthorship W79006051A5048826252 @default.
- W79006051 hasAuthorship W79006051A5069054353 @default.
- W79006051 hasConcept C119857082 @default.
- W79006051 hasConcept C12267149 @default.
- W79006051 hasConcept C127413603 @default.
- W79006051 hasConcept C153180895 @default.
- W79006051 hasConcept C154945302 @default.
- W79006051 hasConcept C201995342 @default.
- W79006051 hasConcept C204321447 @default.
- W79006051 hasConcept C2524010 @default.
- W79006051 hasConcept C2777212361 @default.
- W79006051 hasConcept C2777530160 @default.
- W79006051 hasConcept C2780451532 @default.
- W79006051 hasConcept C28490314 @default.
- W79006051 hasConcept C33923547 @default.
- W79006051 hasConcept C41008148 @default.
- W79006051 hasConcept C90805587 @default.
- W79006051 hasConcept C95623464 @default.
- W79006051 hasConceptScore W79006051C119857082 @default.
- W79006051 hasConceptScore W79006051C12267149 @default.
- W79006051 hasConceptScore W79006051C127413603 @default.
- W79006051 hasConceptScore W79006051C153180895 @default.
- W79006051 hasConceptScore W79006051C154945302 @default.
- W79006051 hasConceptScore W79006051C201995342 @default.
- W79006051 hasConceptScore W79006051C204321447 @default.
- W79006051 hasConceptScore W79006051C2524010 @default.
- W79006051 hasConceptScore W79006051C2777212361 @default.
- W79006051 hasConceptScore W79006051C2777530160 @default.
- W79006051 hasConceptScore W79006051C2780451532 @default.
- W79006051 hasConceptScore W79006051C28490314 @default.
- W79006051 hasConceptScore W79006051C33923547 @default.
- W79006051 hasConceptScore W79006051C41008148 @default.
- W79006051 hasConceptScore W79006051C90805587 @default.
- W79006051 hasConceptScore W79006051C95623464 @default.
- W79006051 hasLocation W790060511 @default.
- W79006051 hasOpenAccess W79006051 @default.
- W79006051 hasPrimaryLocation W790060511 @default.
- W79006051 hasRelatedWork W2041399278 @default.
- W79006051 hasRelatedWork W2041636156 @default.
- W79006051 hasRelatedWork W2099369243 @default.
- W79006051 hasRelatedWork W2136184105 @default.
- W79006051 hasRelatedWork W2160451891 @default.
- W79006051 hasRelatedWork W3194539120 @default.
- W79006051 hasRelatedWork W4223656335 @default.
- W79006051 hasRelatedWork W4242764575 @default.
- W79006051 hasRelatedWork W2187500075 @default.
- W79006051 hasRelatedWork W2345184372 @default.
- W79006051 isParatext "false" @default.
- W79006051 isRetracted "false" @default.
- W79006051 magId "79006051" @default.
- W79006051 workType "book-chapter" @default.