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Bag of words and embedding text representation methods for medical article classification

Cichosz Paweł

International Journal of Applied Mathematics and Computer Science

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2023

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Vol. 33, no. 4

603--621

EN

Text classification has become a standard component of automated systematic literature review (SLR) solutions, where articles are classified as relevant or irrelevant to a particular literature study topic. Conventional machine learning algorithms for tabular data which can learn quickly from not necessarily large and usually imbalanced data with low computational demands are well suited to this application, but they require that the text data be transformed to a vector representation. This work investigates the utility of different types of text representations for this purpose. Experiments are presented using the bag of words representation and selected representations based on word or text embeddings: word2vec, doc2vec, GloVe, fastText, Flair, and BioBERT. Four classification algorithms are used with these representations: a naive Bayes classifier, logistic regression, support vector machines, and random forest. They are applied to datasets consisting of scientific article abstracts from systematic literature review studies in the medical domain and compared with the pre-trained BioBERT model fine-tuned for classification. The obtained results confirm that the choice of text representation is essential for successful text classification. It turns out that, while the standard bag of words representation is hard to beat, fastText word embeddings make it possible to achieve roughly the same level of classification quality with the added benefit of much lower dimensionality and capability of handling out-of-vocabulary words. More refined embeddings methods based on deep neural networks, while much more demanding computationally, do not appear to offer substantial advantages for the classification task. The fine-tuned BioBERT classification model performs on par with conventional algorithms when they are coupled with their best text representation methods.

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A case study in text mining of discussion forum posts: Classification with bag of words and global vectors

Cichosz P.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 4

787--801

EN

Despite the rapid growth of other types of social media, Internet discussion forums remain a highly popular communication channel and a useful source of text data for analyzing user interests and sentiments. Being suited to richer, deeper, and longer discussions than microblogging services, they particularly well reflect topics of long-term, persisting involvement and areas of specialized knowledge or experience. Discovering and characterizing such topics and areas by text mining algorithms is therefore an interesting and useful research direction. This work presents a case study in which selected classification algorithms are applied to posts from a Polish discussion forum devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana. The utility of two different vector text representations is examined: the simple bag of words representation and the more refined embedded global vectors one. While the former is found to work well for the multinomial naive Bayes algorithm, the latter turns out more useful for other classification algorithms: logistic regression, SVMs, and random forests. The obtained results suggest that post-classification can be applied for measuring publication intensity of particular topics and, in the case of forums related to psychoactive substances, for monitoring the risk of drug-related crime.

3

Propozycja mieszanego przetwarzania półstrukturalnego modelu opisu zdarzeń z akcji ratowniczo-gaśniczych Państwowej Straży Pożarnej PSP3

Mirończuk M., Maciak T.

Bezpieczeństwo i Technika Pożarnicza

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2013

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Nr 1

95-106

PL

W opracowaniu przedstawiono aktualnie rozwijane reprezentacje wiedzy i sposoby opisów zdarzeń, dla systemu wnioskowania na podstawie przypadków zdarzeń służb ratowniczych Państwowej Straży Pożarnej PSP. W artykule zaproponowano sposób ich przetwarzania. Przedstawiony sposób bazuje na klasyfikacji i wyszukiwaniu opisów zdarzeń.

EN

This paper describes a review of actual developed knowledge representation and case representation for fire services cases based reasoning system. The article also describes a method of processing the cases of events. This processing method based on classification and information retrieval.