no code implementations • 18 May 2023 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
no code implementations • 11 Feb 2021 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost.
no code implementations • 23 Jul 2020 • Amila Silva, Shanika Karunasekera, Christopher Leckie, Ling Luo
To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams.
no code implementations • 16 Jul 2020 • Amila Silva, Pei-Chi Lo, Ee-Peng Lim
Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
no code implementations • 18 Jun 2020 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations.
no code implementations • 6 Feb 2020 • Amila Silva, Pei-Chi Lo, Ee-Peng Lim
To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions.
no code implementations • 23 Oct 2019 • Amila Silva, Shanika Karunasekera, Christopher Leckie, Ling Luo
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics.
no code implementations • WS 2019 • Amila Silva, Chathurika Amarathunga
Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations.
no code implementations • SEMEVAL 2018 • Ph, Peter i, Amila Silva, Wei Lu
This paper describes the SemEval 2018 shared task on semantic extraction from cybersecurity reports, which is introduced for the first time as a shared task on SemEval.