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- W4386528080 abstract "Recent years have witnessed significant progress in the deployment of advanced Natural Language Processing (NLP) techniques based on transformer technology, across many domains and applications. However, in legal domains, due to the complexity, length, and sparsity of legal case documents, the use of these advanced NLP techniques has offered comparatively slight returns. Perhaps even more importantly, such methods are critically lacking in explainability and justification of outputs, which are essential for many legal applications. We propose that the direction of these NLP techniques should be aimed at ascription to a legal knowledge model, which can then provide the necessary and auditable justifications for the rationale of any case outcome. In this paper we investigate the effectiveness of using Hierarchical Bidirectional Encoder Representations from Transformers (H-BERT) models to ascribe to an Angelic Domain Model (ADM) that is able to represent the legal knowledge of a domain in a structured way, enabling justifications and improving performance. Our study involved an annotation task on a popular domain, cases from the European Court of Human Rights, to gain an understanding of the balance of complaints in the domain. The data set produced from this study enabled training of models for factor ascription using the classification targets derived from the annotations. We present results of experiments conducted to evaluate the performance of the ascription task at three different levels of abstraction within the structured model." @default.
- W4386528080 created "2023-09-08" @default.
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- W4386528080 date "2023-06-19" @default.
- W4386528080 modified "2023-10-16" @default.
- W4386528080 title "Combining a Legal Knowledge Model with Machine Learning for Reasoning with Legal Cases" @default.
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- W4386528080 doi "https://doi.org/10.1145/3594536.3595158" @default.
- W4386528080 hasPublicationYear "2023" @default.
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