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pro vyhledávání: '"Emil Rijcken"'
Autor:
Emil Rijcken, Kalliopi Zervanou, Pablo Mosteiro, Floortje Scheepers, Marco Spruit, Uzay Kaymak
Publikováno v:
Natural Language Processing Journal, Vol 8, Iss , Pp 100082- (2024)
Topic modeling is a prevalent task for discovering the latent structure of a corpus, identifying a set of topics that represent the underlying themes of the documents. Despite its popularity, issues with its evaluation metric, the coherence score, re
Externí odkaz:
https://doaj.org/article/ad068e9e2dfe42f5b9e81a94730ce8f5
Autor:
Emil Rijcken, Uzay Kaymak, Floortje Scheepers, Pablo Mosteiro, Kalliopi Zervanou, Marco Spruit
Publikováno v:
Frontiers in Big Data, Vol 5 (2022)
The clinical notes in electronic health records have many possibilities for predictive tasks in text classification. The interpretability of these classification models for the clinical domain is critical for decision making. Using topic models for t
Externí odkaz:
https://doaj.org/article/4ccd18d0ef3742ba892d69b0a4a4b5e5
Autor:
Emil Rijcken, Kalliopi Zervanou, Pablo Mosteiro, Floortje Scheepers, Marco Spruit, Uzay Kaymak
Throughout the history of artificial intelligence, various algorithm branches have predominantly been used at different times. The last decade has been characterized by a shift from rule-based methods to self-learning methods. However, while the shif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39cf764138951490a13b860b5064054b
https://doi.org/10.21203/rs.3.rs-2320804/v1
https://doi.org/10.21203/rs.3.rs-2320804/v1
Autor:
Emil Rijcken, Pablo Mosteiro, Kalliopi Zervanou, Marco Spruit, Floortje Scheepers, Uzay Kaymak
Publikováno v:
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b685ac0e05c17d808131fbe47b68ee3
https://doi.org/10.1109/fuzz-ieee55066.2022.9882661
https://doi.org/10.1109/fuzz-ieee55066.2022.9882661
Autor:
Emil Rijcken, Floortje Scheepers, Pablo Mosteiro, Kalliopi Zervanou, Marco Spruit, Uzay Kaymak
Publikováno v:
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
In many domains that employ machine learning models, both high performing and interpretable models are needed. A typical machine learning task is text classification, where models are hardly interpretable. Topic models, used as topic embeddings, carr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8027cfb1b6fe4685a373133e94a88ff5
https://research.tue.nl/en/publications/e6ce17b2-de9a-467f-8c68-b6f8b2ce09ac
https://research.tue.nl/en/publications/e6ce17b2-de9a-467f-8c68-b6f8b2ce09ac