Human Language and Beyond..

We focus on deeply understanding human language and modelling them using leading-edge NLP, machine learning, and deep learning technologies to solve language-related societal problems #LT4SG

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Welcome back Georgia and Dasuni..

LT4SG welcome back Georgia Zhang as a Honors student and Dasuni Jayawickrama as an Advanced Topic CS student to work in research related to Emotion-Cause modeling from unstructured text. Georgia worked with LT4SG in her previous two research projects and contributed lot to the advancement of cyberbullying research. She currently works at DSTO while conducting her studies at the University of Adelaide. We are glad to have Georgia back and wish her good luck in her honors year..!! Dasuni worked with LT4SG as a research assistant to conduct a systematic … Continue reading Welcome back Georgia and Dasuni..

Congratulations Menasha for submitting thesis..!!

Huge congratulations to LT4SG PhD student Menasha Thilakaratne for submitting her thesis on her amazing research on ‘Literature Based Discovery (LBD)’. Menasha joined LT4SG in 2017 and established LBD research area within LT4SG. She has published her research in many highly-ranked venues. In Search of a Common Thread: Enhancing the LBD Workflow with a view to Widespread Applicability Literature-Based Discovery (LBD) research focuses on discovering implicit new knowledge linkages from existing scientific facts to provide impetus to research progress and increase research productivity. Despite the significant progress of LBD research, … Continue reading Congratulations Menasha for submitting thesis..!!

Dr Thushari Atapattu is one of the finalists in the Women in AI awards

Congratulations to Dr Thushari Atapattu from the Language Technology for Social Good (LT4SG) group who has been confirmed as a finalist in the Women in AI Awards for Australia and New Zealand.  There are 11 categories in the Women in AI Awards , covering different sectors and applications of AI (e.g. Health, Defence, Mining, Cyber Security etc), and Dr Atapattu is one of 3 finalists in the ‘AI in Education’ category. Each of the 11 award categories are judged on innovation, leadership and inspiring potential, global potential and impact, and ability for … Continue reading Dr Thushari Atapattu is one of the finalists in the Women in AI awards

Join our talk in ALTA Workshop!

LT4SG researchers are excited to present their work on “Automatic Detection of Cyberbullying against Women and Immigrants and Cross-domain Adaptability” in upcoming Australasian Language Technology Association 2020. “Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 – Task 5 (HatEval) … Continue reading Join our talk in ALTA Workshop!

See you @COLING!

LT4SG researchers have competed for the SemEval 2020 challenge on ‘Offensive Language Classification (Task 12). We are thrilled to achieve over 0.9 of F-measure for offensive language identification sub task. Our paper titled “AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media” will be presented in the International Workshop on Semantic Evaluation at COLING2020. “This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – … Continue reading See you @COLING!

Check out our talk @EMNLP!

LT4SG researchers presented their work on “Enhancing the detection of Cyberbullying through participant roles” in the Workshop on Online Abuse and Harm at EMNLP2020. “Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We … Continue reading Check out our talk @EMNLP!

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