Show simple item record

dc.contributor.authorLu, Jinghui
dc.contributor.authorHenchion, Maeve
dc.contributor.authorMac Namee, Brian
dc.identifier.citationLu, Jinghui, Maeve Henchion, and Brian Mac Namee. "Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets." arXiv preprint arXiv:1910.03505 (2019).
dc.description.abstractManually labelling large collections of text data is a timeconsuming and expensive task, but one that is necessary to support machine learning based on text datasets. Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them. The representation mechanism used to represent text documents when performing active learning, however, has a significant influence on how effective the process will be. While simple vector representations such as bag-of-words have been shown to be an effective way to represent documents during active learning, the emergence of representation mechanisms based on the word embeddings prevalent in neural network research (e.g. word2vec and transformer based models like BERT) offer a promising, and as yet not fully explored, alternative. This paper describes a large-scale evaluation of the effectiveness of different text representation mechanisms for active learning across 8 datasets from varied domains. This evaluation shows that using representations based on modern word embeddings, especially BERT, which have not yet been widely used in active learning, achieves a significant improvement over more commonly used vector representations like bag-of-words.en_US
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.subjectactive learningen_US
dc.subjecttext classificationen_US
dc.subjectword embeddingsen_US
dc.titleInvestigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasetsen_US

Files in this item

main article

This item appears in the following Collection(s)

Show simple item record

Attribution-ShareAlike 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-ShareAlike 3.0 United States