Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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    PublicationEmbargo
    Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review
    (Springer, Cham, 2022-09-19) Panchendraraja, R; Saxena, A
    The advent of social media in day-to-day life has made communications between people more often and easier than ever before. Analyzing the content in social media has opened up a massive amount of research and commercial opportunities. However, the content in social media is noisy and multi-lingual, which postures computational challenges ahead. Especially, the non-native English speakers and writers tend to mix their native language with English while generating social media content. Thus it requires a comprehensive prepossessing of text, including the identification of language for many language processing applications. In the area of language processing, deep learning has shown to be very successful, and the latest research works have witnessed the adoption of deep learning solutions to cater to the challenges in analyzing code-mixed text. Here, we highlight a comprehensive study of deep learning techniques used for analyzing the code-mix text of social media to understand the state-of-the-art and existing research challenges. We will discuss several applications of code-mixed text analysis and future directions.
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    PublicationOpen Access
    UNDERSTANDING CONSTRUCTION SITE SAFETY HAZARDS THROUGH OPEN DATA: TEXT MINING APPROACH
    (researchgate.net, 2021-10) Rupasinghe, N. K. A. H; Panuwatwanich, K
    Construction is an industry well known for its very high rate of injuries and accidents around the world. Even though many researchers are engaged in analysing the risks of this industry using various techniques, construction accidents still require much attention in safety science. According to existing literature, it has been found that hazards related to workers, technology, natural factors, surrounding activities and organisational factors are primary causes of accidents. Yet, there has been limited research aimed to ascertain the extent of these hazards based on the actual reported accidents. Therefore, the study presented in this paper was conducted with the purpose of devising an approach to extract sources of hazards from publicly available injury reports by using Text Mining (TM) and Natural Language Processing (NLP) techniques. This paper presents a methodology to develop a rule-based extraction tool by providing full details of lexicon building, devising extraction rules and the iterative process of testing and validation. In addition, the developed rule-based classifier was compared with, and found to outperform, the existing statistical classifiers such as Support Vector Machine (SVM), Kernel SVM, K-nearest neighbours, Naïve Bayesian classifier and Random Forest classifier. The finding using the developed tool identified the worker factor as the highest contributor to construction site accidents followed by technological factor, surrounding activities, organisational factor, and natural factor (1%). The developed tool could be used to quickly extract the sources of hazards by converting largely available unstructured digital accident data to structured attributes allowing better data-driven safety management.