@Article{cmc.2023.034157,
AUTHOR = {Sher Muhammad Daudpota, Saif Hassan, Yazeed Alkhurayyif, Abdullah Saleh Alqahtani, Muhammad Haris Aziz},
TITLE = {Active Learning Strategies for Textual Dataset-Automatic Labelling},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {2},
PAGES = {1409--1422},
URL = {http://www.techscience.com/cmc/v76n2/54029},
ISSN = {1546-2226},
ABSTRACT = {The Internet revolution has resulted in abundant data from various
sources, including social media, traditional media, etcetera. Although the
availability of data is no longer an issue, data labelling for exploiting it in
supervised machine learning is still an expensive process and involves tedious
human efforts. The overall purpose of this study is to propose a strategy
to automatically label the unlabeled textual data with the support of active
learning in combination with deep learning. More specifically, this study
assesses the performance of different active learning strategies in automatic
labelling of the textual dataset at sentence and document levels. To achieve
this objective, different experiments have been performed on the publicly
available dataset. In first set of experiments, we randomly choose a subset
of instances from training dataset and train a deep neural network to assess
performance on test set. In the second set of experiments, we replace the
random selection with different active learning strategies to choose a subset
of the training dataset to train the same model and reassess its performance
on test set. The experimental results suggest that different active learning
strategies yield performance improvement of 7% on document level datasets
and 3% on sentence level datasets for auto labelling.},
DOI = {10.32604/cmc.2023.034157}
}