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Robert Amor

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3

AAAI Conference 2024 Conference Paper

Robust Node Classification on Graph Data with Graph and Label Noise

  • Yonghua Zhu
  • Lei Feng
  • Zhenyun Deng
  • Yang Chen
  • Robert Amor
  • Michael Witbrock

Current research for node classification focuses on dealing with either graph noise or label noise, but few studies consider both of them. In this paper, we propose a new robust node classification method to simultaneously deal with graph noise and label noise. To do this, we design a graph contrastive loss to conduct local graph learning and employ self-attention to conduct global graph learning. They enable us to improve the expressiveness of node representation by using comprehensive information among nodes. We also utilize pseudo graphs and pseudo labels to deal with graph noise and label noise, respectively. Furthermore, We numerically validate the superiority of our method in terms of robust node classification compared with all comparison methods.

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ECAI Conference 2023 Conference Paper

Towards Legal Judgment Summarization: A Structure-Enhanced Approach

  • Qiqi Wang 0005
  • Ruofan Wang
  • Kaiqi Zhao 0001
  • Robert Amor
  • Benjamin Liu
  • Xianda Zheng
  • Zeyu Zhang 0004
  • Zijian Huang 0003

Judgment summaries are beneficial for legal practitioners to comprehend and retrieve case law efficiently. Unlike summaries in general domains, e. g. , news, judgment summaries often require a clear structure. Such a structure helps readers grasp the information contained in the summary and reduces information loss. To the best of our knowledge, none of the existing text summarizers can generate summaries aligned with the summary structure in the legal domain. Inspired by this observation, this paper introduces a Summary Structure-Enhanced (SSE) method to synthesize structured summaries for legal documents. SSE can easily be incorporated into the Encoder-Decoder framework, which is commonly adopted in state-of-the-art text summarizers. Experiments on the datasets of New Zealand and Chinese judgments show that the proposed method consistently improves the performance of state-of-the-art summarizers in terms of Rouge scores.

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AILAW Journal 2018 Journal Article

Maintainable process model driven online legal expert systems

  • Johannes Dimyadi
  • Sam Bookman
  • David Harvey
  • Robert Amor

Abstract Legal expert systems are computer applications that can mimic the consultation process of a legal expert to provide advice specific to a given scenario. The core of these systems is the experts’ knowledge captured in a sophisticated and often complex logic or rule base. Such complex systems rely on both knowledge engineers or system programmers and domain experts to maintain and update in response to changes in law or circumstances. This paper describes a pragmatic approach using process modelling techniques that enables a complex legal expert system to be maintained and updated dynamically by a domain expert such as a legal practitioner with little computing knowledge. The approach is illustrated using a case study on the design of an online expert system that allows a user to navigate through complex legal options in the domain of International Family Law.

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