Abstract
An integral task for many natural language processing applications is the extraction of the narrative process described in a document. For understanding such processes we need to recognize the mentioned events and their temporal component. With this information, we can understand the sequence of events i.e. construct a timeline. The main task dealing with the temporal component of events is temporal relation extraction. The goal of temporal relation extraction is to determine how the times of two events are related to one another. For example, such relation would tell us whether one event happened before or after another one. In this paper, we propose a novel architecture for a temporal relation extraction model combining text information with information captured in the form of a temporal event graph. We present our initial results on the domain of clinical documents. Using a temporal event graph with only correct relations, the model achieves F1 score of 83.6% which is higher than any of our state-of-the-art baseline models. This shows the promise of our proposed approach.
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This work has been financially supported by the Slovenian Research Agency in the young researchers grant.
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Knez, T., Žitnik, S. (2023). Temporal Relation Extraction from Clinical Texts Using Knowledge Graphs. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-031-33080-3_30
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DOI: https://doi.org/10.1007/978-3-031-33080-3_30
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