Abstract
Most research related to text processing focus on implementing algorithms solving complex tasks, considering simple preprocessing tools as acceptable together with their shortcomings. However, the performance of these low-level tools is sometimes far from being perfect, and errors introduced by them in a text processing chain propagate to higher level modules. In this research, our goal was to create an algorithm that can be used to improve the accuracy of a neglected, but important low-level tool, the lemmatizer. In order to achieve this goal, we experimented with a recurrent neural network classifier and an SVM-based classifier using a word embedding representation of word forms. Our system is able to predict with high accuracy (92.13%) whether a lemma candidate assigned to a word form is correct with the given part-of-speech.
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Acknowledgments
This research has been implemented with support provided by grants FK125217 and PD125216 of the National Research, Development and Innovation Office of Hungary financed under the FK17 and PD17 funding schemes.
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Novák, A., Novák, B. (2023). Identification of Lemmatization Errors Using Neural Models. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham. https://doi.org/10.1007/978-3-031-23793-5_32
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