Now showing items 1-10 of 10

    • Hardness-guided domain adaptation to recognize biomedical named entities under low-resource scenarios 

      Buntine, Wray; Chen, Changyou; Beare, Richard; nguyen, Ngoc Dang; Du, Lan (2022)
      Domain adaptation is a promising approach to address data scarcity in low-resource scenarios. However, applying it to token-level tasks like biomedical Named Entity Recognition (bioNER) poses challenges due to the unique ...
    • On the effect of isotropy on VAE representations of text 

      Buntine, Wray; Zhang, Lan; Shareghi, Ehsan (2022-05)
      Injecting desired geometric properties into text representations has attracted a lot of attention. A property that has been argued for, due to its better utilisation of representation space, is isotropy. In parallel, VAEs ...
    • Understanding Hierarchical Processes 

      Buntine, Wray (2022-11-22)
      Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for ...
    • Understanding Hierarchical Processes 

      Wray, Butine (2022-12)
      Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for ...
    • Cross-domain graph anomaly detection via anomaly-aware contrastive alignment 

      Wang, Qizhou; Pang, Guansong; Salehi, Mahsa; Buntine, Wray; Leckie, Christopher (2022-12-02)
      Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it ...
    • A systematic review of the use of topic models for short text social media analysis 

      Doogan, Caitlin Poet Laureate; Buntine, Wray; Linger, Henry (2023-03-14)
      Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures ...
    • Does informativeness matter? Active learning for educational dialogue act classification 

      Tan, Wei; Lin, Jionghao; Lang, David; Chen, Guanliang; Gasevic, Dragan; Du, Lan; Buntine, Wray (2023-04-12)
      Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manual annotation ...
    • AUC Maximization for Low-Resource Named Entity Recognition 

      Nguyen, Ngoc Dang; Tan, Wei; Du, Lan; Buntine, Wray; Beare, Richard; Chen, Changyou (2023-04-13)
      Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective ...
    • Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets 

      Lin, Jionghao; Tan, Wei; Nguyen, Ngoc Dang; Lang, David; Du, Lan; Buntine, Wray; Beare, Richard; Chen, Guanliang; Gašević, Dragan (2023-04-15)
      Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based ...
    • A systematic review of the use of topic models for short text social media analysis 

      Doogan, Caitlin; Buntine, Wray; Linger, Henry (2023-05-01)
      Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures ...

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