Automated Extraction of ABAC Policies from Natural-Language Documents in Healthcare Systems

Xia, Yutang; Zhai, Shengfang; Wang, Qinting; Hou, Huiting; Wu, Zhonghai; Shen, Qingni

doi:10.1109/bibm55620.2022.9995559

Cited by 1 publication

(4 citation statements)

References 23 publications

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“…Selected Studies Precision, recall, F1 score [112], [38], [37], [56], [54], [53], [39], [28], [59], [77], [65], [66], [47], [36], [49], [74], [50], [22], [59], [52], [55], [21], [35], [113], [32], [68], [62], [41], [40], [60], [33], [81], [57], [76], [46], [71], [44], [48] Alignment with gold standard [114], [112], [22], [60], [76], [38]…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…• Requires a large training dataset. Data noise can occur during SRL due to inconsistencies in human annotation data that can introduce noise, making it difficult for the model to understand the semantic role of words accurately [66]. In addition, data noise problems can originate from SRL models that are sensitive to the sentence sequence of the input text (for example, RNN), where there is noise at one point in the sentence sequence, which will cause errors in capturing the overall information context [50].…”

Section: Hybrid Neural Networkmentioning

confidence: 99%

“…After analyzing and discussing the findings, we identified several directions for developing SRL for information extraction from low-resource data. VOLUME XX, 2017 [50], [118], [52], [39], [66], [28], [62], [65], [37] Extensive data labeling [39], [65], [28] Data quality [28] Noise [50], [66] Out-of-Vocabulary [67], [65], [85] Language Ambiguity [66], [65], [37] Morphology [50], [47], [42] Linguistic diversity [50], [47], [28], [85], [42] Differences in sentence structure [50], [52] Incomplete sentences [66] Complex sentences [39], [66], [65], [28], [42]…”

Section: B Prospect For Future Researchmentioning

confidence: 99%

“…Use of specific context [66], [67], [65], [37] Use of specific terminology [66], [65], [37], [60] Capability Selecting appropriate features [52] Generalization [65], [37], [52]…”

Section: Domainmentioning

confidence: 99%

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A Systematic Review on Semantic Role Labeling for Information Extraction in Low-Resource Data

Ariyanto,

Purwitasari,

Fatichah

2024

IEEE Access

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Challenges in the big data phenomenon arise due to the existence of unstructured text data, which is very large, comes from various sources, has various formats, and contains much noise. The complexity of unstructured text data makes it difficult to extract useful information. Therefore, a process is needed to transform it into structured data to be processed further. The information Extraction (IE) process helps to extract relationships, entities, semantic roles, and events from unstructured text data by converting them into structured output. One of IE's tasks is Semantic Role Labeling (SRL), which has a crucial function in identifying semantic roles in a sentence so that it can enrich the understanding of the text. However, much of SRL development focuses on high-resource data, especially in English. The limited development of SRL in specific low-resource languages or domains is a complex challenge. This research aims to conduct a systematic study on the development of SRL for low-resource data, both in low-resource language or domainspecific contexts. The review process was carried out systematically using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, and 54 quality papers were obtained from the filtering process (from 2018 to 2023). We review several essential points, including (1) datasets that are often used for SRL tasks and their labeling strategies for low-resource data, (2) methods that have currently been developed for SRL tasks and learning scenarios when dealing with low-resource data, (4) evaluation metrics, (5) application of SRL tasks. This review is complemented by a discussion of issues and potential solutions for developing SRL on low-resource data to help researchers develop SRL more effectively in dealing with the challenges faced with low-resource data.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Hybrid Neural Networkmentioning

confidence: 99%

Section: B Prospect For Future Researchmentioning

confidence: 99%

“…Use of specific context [66], [67], [65], [37] Use of specific terminology [66], [65], [37], [60] Capability Selecting appropriate features [52] Generalization [65], [37], [52]…”

Section: Domainmentioning

confidence: 99%

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