Cofee: A Comprehensive Ontology for Event Extraction from Text

Balali, Ali; Asadpour, Masoud; Jafari, Seyed Hossein

doi:10.2139/ssrn.4117538

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…If directly applied the existing extraction technologies to the judicial field, they will face the problem of incident type mismatch (Filtz et al, 2020; Li et al, 2020). In addition, many extraction technologies (e.g., Balali et al, 2021; Chen et al, 2015; Nguyen et al, 2016) were experimentally verified on publicly available English data sets, but in the Chinese judicial field, as far as we know, there was no standard experimental data (Feng et al, 2022). Furthermore, in Chinese divorce legal cases, there are many sentences containing multiple events that share arguments or trigger words, but most of the existing technologies are focused on extracting a single event from a single sentence (Zeng et al, 2018), which cannot solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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In the formal procedure of Chinese civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching and regular matching, to get the target information. These approaches are time‐consuming and heavily depend on prior knowledge and the carefulness of the officers. To assist the officers in enhancing working efficiency and accuracy, we conduct a case study of detecting disputes from Chinese divorce cases based on proposing a Two‐Round‐Labeling (TRL) event extracting technique in this article. We implement the Judicial Intelligent Assistant (JIA) system according to the proposed approach to (1) automatically extract focus events from divorce case materials, (2) align events by identifying co‐reference among them, and (3) detect conflicts among events brought by the plaintiff and the defendant. With the JIA system, it is convenient for judges to determine the disputed issues in Chinese divorce cases. Experimental results demonstrate that the proposed approach and system can obtain the focus of Chinese divorce cases and detect conflicts more effectively and efficiently compared with the existing method.

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Section: Introductionmentioning

confidence: 99%

Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge

Zhang,

Li,

Sheng

et al. 2024

Expert Systems

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“…To date, there is no unified classification framework or standard for the news announcements regarding the Chinese stock market. Therefore, the existing research has constructed Electronics 2022, 11, 2058 2 of 17 various event type frameworks using expert domain knowledge and experience [2], clustering [3], ontology [4], and other methods [5]. Some studies have implemented a fine-grained event type framework.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Classification of Announcement News Events in the Chinese Stock Market

et al. 2022

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Determining the event type is one of the main tasks of event extraction (EE). The announcement news released by listed companies contains a wide range of information, and it is a challenge to determine the event types. Some fine-grained event type frameworks have been built from financial news or stock announcement news by domain experts manually or by clustering, ontology or other methods. However, we think there are still some improvements to be made based on the existing results. For example, a legal category has been created in previous studies, which considers violations of company rules and violations of the law the same thing. However, the penalties they face and the expectations they bring to investors are different, so it is more reasonable to consider them different types. In order to more finely classify the event type of stock announcement news, this paper proposes a two-step method. First, the candidate event trigger words and co-occurrence words satisfying the support value are extracted, and they are arranged in the order of common expressions through the algorithm. Then, the final event types are determined using three proposed criteria. Based on the real data of the Chinese stock market, this paper constructs 54 event types (p = 0.927, f = 0.946), and some reasonable and valuable types have not been discussed in previous studies. Finally, based on the unilateral trading policy of the Chinese stock market, we screened out some event types that may not be valuable to investors.

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