Abstract
Words don’t come easy, which fosters the use of generative artificial intelligence models in ongoing popularity of widely available applications such as ChatGPT. The result is an even greater flood of online content that takes time to process. It is where Natural Language Processing tools for classification come in handy. Distinguishing fake news, event types, and other tasks can help process everyday information. In practice, such systems must work on data streams where fast prediction is needed. To achieve it, methods not based on neural networks can be used. Instead, they require feature extractions from the text to convert it to the model input. The primary methods used for this purpose are bag-of-words and n-grams, which allow converting the corpus of texts into a numerical format. This paper proposes a new strategy for creating n-grams – Hollow n-grams – which can be used to create classifiers ensembles with a higher generalization ability than models based on regular n-grams only.
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Acknowledgement
This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology.
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Borek-Marciniec, W., Ksieniewicz, P. (2023). Hollow n-grams Vectorizer for Natural Language Processing Problems. In: Burduk, R., Choraś, M., Kozik, R., Ksieniewicz, P., Marciniak, T., Trajdos, P. (eds) Progress on Pattern Classification, Image Processing and Communications. CORES IP&C 2023 2023. Lecture Notes in Networks and Systems, vol 766. Springer, Cham. https://doi.org/10.1007/978-3-031-41630-9_2
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