Abstract
There is a growing interest in automatic text processing and knowledge extraction from text repositories which often requires building new language resources and technologies. We present the KEMMRL model designed for the under-resourced but morphologically rich Croatian language. The proposed model uses natural language processing techniques, state-of-the-art deep learning algorithms and a rule-based approach to generate knowledge representations. The output of the newly developed HRtagger and HRparser methods in combination with the KEMMRL model is knowledge represented in the form of an ordered recursive hypergraph. Since the performance of KEMMRL is highly dependent on the applied deep learning methods, we evaluated them using hr500k reference corpus in the training and testing phase and manually designed out-of-domain Semantic Hypergraph Corpus (SemCro). The results of standard evaluation metrics showed that the HRtagger and HRparser achieved significantly better results than other state-of-the-art methods. These methods also showed the best results in measuring the structural similarity of hypergraphs, the highest average similarity to the manually annotated semantic hypergraphs and the number of semantic hyperedges correctly annotated by the model. The semantic hypergraph proved to be an ideal structure to capture and represent knowledge from more complex sentences without information loss. Researchers and developers of similar morphologically rich languages can customize and extend KEMMRL to their requirements. This article highlights the potential benefits of implementing the KEMMRL model into an Intelligent Tutoring System (ITS), and future research may focus on developing and testing such implementations.
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The paper is part of the work supported by the Office of Naval Research Grant No.N00014-20-1-2066
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Vasić, D., Žitko, B., Grubišić, A., Gašpar, A. (2023). KEMMRL: Knowledge Extraction Model for Morphologically Rich Languages. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_19
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