Ruben Van Heusden

Also published as: Ruben van Heusden


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Timeline Extraction from Decision Letters Using ChatGPT
Femke Bakker | Ruben Van Heusden | Maarten Marx
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)

Freedom of Information Act (FOIA) legislation grants citizens the right to request information from various levels of the government, and aims to promote the transparency of governmental agencies. However, the processing of these requests is often met with delays, due to the inherent complexity of gathering the required documents. To obtain accurate estimates of the processing times of requests, and to identify bottlenecks in the process, this research proposes a pipeline to automatically extract these timelines from decision letters of Dutch FOIA requests. These decision letters are responses to requests, and contain an overview of the process, including when the request was received, and possible communication between the requester and the relevant agency. The proposed pipeline can extract dates with an accuracy of .94, extract event phrases with a mean ROUGE- L F1 score of .80 and can classify events with a macro F1 score of .79.Out of the 50 decision letters used for testing (each letter containing one timeline), the model correctly classified 10 of the timelines completely correct, with an average of 3.1 mistakes per decision letter.


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Entity Linking in the ParlaMint Corpus
Ruben van Heusden | Maarten Marx | Jaap Kamps
Proceedings of the Workshop ParlaCLARIN III within the 13th Language Resources and Evaluation Conference

The ParlaMint corpus is a multilingual corpus consisting of the parliamentary debates of seventeen European countries over a span of roughly five years. The automatically annotated versions of these corpora provide us with a wealth of linguistic information, including Named Entities. In order to further increase the research opportunities that can be created with this corpus, the linking of Named Entities to a knowledge base is a crucial step. If this can be done successfully and accurately, a lot of additional information can be gathered from the entities, such as political stance and party affiliation, not only within countries but also between the parliaments of different countries. However, due to the nature of the ParlaMint dataset, this entity linking task is challenging. In this paper, we investigate the task of linking entities from ParlaMint in different languages to a knowledge base, and evaluating the performance of three entity linking methods. We will be using DBPedia spotlight, WikiData and YAGO as the entity linking tools, and evaluate them on local politicians from several countries. We discuss two problems that arise with the entity linking in the ParlaMint corpus, namely inflection, and aliasing or the existence of name variants in text. This paper provides a first baseline on entity linking performance on multiple multilingual parliamentary debates, describes the problems that occur when attempting to link entities in ParlaMint, and makes a first attempt at tackling the aforementioned problems with existing methods.