Fumito Masui
Czasopismo Techniczne, Mechanika Zeszyt 2-M (7) 2015, 2015, s. 230 - 243
This paper presents a modular system for the support of experiments and research in text classification. Usually the research process requires two general kinds of abilities. Firstly, to laboriously analyse the provided data, perform experiments and from the experiment results create materials for preparing a scientific paper such as tables or graphs. The second kind of task includes, for example, providing a creative discussion of the results. To help researchers and allow them to focus more on creative tasks, we provide a system which helps performing the laborious part of research. The system prepares datasets for experiments, automatically performs the experiments and from the results calculates the scores of Precision, Recall, F-score, Accuracy, Specificity and phi-coefficient. It also creates tables in the LaTex format containing all the results and it draws graphs depicting and informatively comparing each group of results.
Fumito Masui
Czasopismo Techniczne, Volume 11 Year 2017 (114), 2017, s. 183 - 197
https://doi.org/10.4467/2353737XCT.17.199.7428W niniejszym artykule przedstawiono metaanalizę badań przeprowadzonych za pomocą kombinatoryki językowej (language combinatorics, LC), nowej metody generacji modelu języka i ekstrakcji cech, opartej o kombinacyjne manipulacje na elementach zdań (np. słowa). W trakcie ostatnich lat LC została zastosowana do wielu zadań z dziedziny klasyfikacji tekstu, takich jak analiza afektu, wykrywanie cyberagresji lub ekstrakcja odniesień do przyszłych wydarzeń. W niniejszym artykule podsumowujemy dwa z najbardziej obszernych doświadczeń i omawiamy ogólne implikacje dotyczące przyszłych zastosowań kombinatoryjnego modelu języka.
Fumito Masui
Czasopismo Techniczne, Mechanika Zeszyt 2-M (7) 2015, 2015, s. 193 - 201
In this paper, we present our research on modeling learning motivation of students by analyzing class evaluation questionnaires, carried out with students as respondents at the end of each term to improve the courses in future semesters. We firstly defined three elements influencing learning motivation: (1) interest, (2) usefulness in the future and (3) satisfaction. Original questionnaire enquiring about those three elements was designed and conducted in multiple classes across different school years. Next, we conducted an experiment to classify students’ motivation for learning using the provided answers. The results of the experiment showed that students’ learning motivation can be estimated using the three elements defined in this study.
Fumito Masui
Czasopismo Techniczne, Volume 4 Year 2018 (115), 2018, s. 189 - 194
https://doi.org/10.4467/2353737XCT.18.066.8378