Exploring the use of pre-trained transformer-based models and semi-supervised learning to build training sets for text classification

Clicks: 1
ID: 287699
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #3,751 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Data annotation is the process of labeling text, images, or other types of content for machine learning tasks. With the rise in popularity of machine learning for classification tasks, large amounts of labeled data is typically desired to train effective models using different algorithms and architectures. Data annotation is a critical step in developing these models and, while there is an abundance of unlabeled data that is being generated everyday, annotation is often a laborious and costly process. Furthermore, low-resource languages such as Filipino do not have as many readily available datasets as mainstream languages that can be leveraged to fine-tune existing models that were pre-trained with large amounts of data. In this study, we explored the use of BERT and semi-supervised learning for textual data in order to see how it might ease the burden of human annotation when building text classification training sets and at the same time reduce the amount of manually-labeled data needed to fine-tune a pre-trained model for a specific downstream text classification task. We then analyzed relevant factors that may affect pseudo-labeling performance, and also compared the accuracy scores of different non-BERT classifiers when trained with the same samples having solely human-labeled data versus its counterpart composed of a mixture of human-labeled data and pseudo-labeled data after semi-supervised learning.
Reference Key
persistent_1760662391_68f19377e51a2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Te, Gian Marco I.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_softtech/6
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.