ORCID Profile
0000-0003-1004-4085
Current Organisations
Queensland University of Technology
,
Comilla University
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Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 11-2023
Publisher: Springer Singapore
Date: 2019
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 11-2022
Publisher: IEEE
Date: 12-2020
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Association for Computing Machinery (ACM)
Date: 11-02-2021
DOI: 10.1145/3446343
Abstract: Language model (LM) has become a common method of transfer learning in Natural Language Processing (NLP) tasks when working with small labeled datasets. An LM is pretrained using an easily available large unlabelled text corpus and is fine-tuned with the labelled data to apply to the target (i.e., downstream) task. As an LM is designed to capture the linguistic aspects of semantics, it can be biased to linguistic features. We argue that exposing an LM model during fine-tuning to instances that capture erse semantic aspects (e.g., topical, linguistic, semantic relations) present in the dataset will improve its performance on the underlying task. We propose a Mixed Aspect S ling (MAS) framework to s le instances that capture different semantic aspects of the dataset and use the ensemble classifier to improve the classification performance. Experimental results show that MAS performs better than random s ling as well as the state-of-the-art active learning models to abuse detection tasks where it is hard to collect the labelled data for building an accurate classifier.
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 22-02-2018
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Publisher: Informa UK Limited
Date: 13-07-2022
Publisher: Springer Science and Business Media LLC
Date: 29-07-2021
Publisher: Springer Singapore
Date: 2021
Publisher: Springer Singapore
Date: 2021
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2017
DOI: 10.1145/3094786
Abstract: Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users’ preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users’ specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users’ specific interests, and hence to improve the performance of IF and IR.
Publisher: Springer Nature Singapore
Date: 2023
Publisher: IEEE
Date: 03-2009
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 25-07-2022
DOI: 10.1007/S13278-022-00917-5
Abstract: Social media platforms have become a common place for information exchange among their users. People leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights into local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, topic modelling, sentiment detection, and semantic brand score to obtain an insight into the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government-reported instances.
Publisher: Wiley
Date: 26-07-2017
DOI: 10.1111/COIN.12133
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/WI.2016.0025
Publisher: IEEE
Date: 03-2009
Publisher: Springer Science and Business Media LLC
Date: 18-06-2020
Publisher: IEEE
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 15-06-2021
Publisher: Elsevier BV
Date: 02-2023
Publisher: SAGE Publications
Date: 03-2023
DOI: 10.1177/08944393231158788
Abstract: Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.
No related grants have been discovered for Md Abul Bashar.