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pro vyhledávání: '"Couto, Francisco M"'
State-of-the-art deep learning entity linking methods rely on extensive human-labelled data, which is costly to acquire. Current datasets are limited in size, leading to inadequate coverage of biomedical concepts and diminished performance when appli
Externí odkaz:
http://arxiv.org/abs/2407.06292
Biomedical Natural Language Processing (NLP) tends to become cumbersome for most researchers, frequently due to the amount and heterogeneity of text to be processed. To address this challenge, the industry is continuously developing highly efficient
Externí odkaz:
http://arxiv.org/abs/2308.05609
Autor:
Nunes, Sérgio, Little, Suzanne, Bhatia, Sumit, Boratto, Ludovico, Cabanac, Guillaume, Campos, Ricardo, Couto, Francisco M., Faralli, Stefano, Frommholz, Ingo, Jatowt, Adam, Jorge, Alípio, Marras, Mirko, Mayr, Philipp, Stilo, Giovanni
ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the
Externí odkaz:
http://arxiv.org/abs/2005.06748
Question Answering (QA) is a natural language processing task that aims at obtaining relevant answers to user questions. While some progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the comp
Externí odkaz:
http://arxiv.org/abs/2002.02375
Autor:
Couto, Francisco M.
This open access book is a step-by-step introduction on how shell scripting can help solve many of the data processing tasks that Health and Life specialists face everyday with minimal software dependencies. The examples presented in the book show ho
Externí odkaz:
http://library.oapen.org/handle/20.500.12657/22825
Recommending Chemical Compounds of interest to a particular researcher is a poorly explored field. The few existent datasets with information about the preferences of the researchers use implicit feedback. The lack of Recommender Systems in this part
Externí odkaz:
http://arxiv.org/abs/2001.07440
Autor:
Sousa, Diana, Couto, Francisco M.
Publikováno v:
Advances in Information Retrieval: 42nd European Conference on IR Research, Volume 12036. 2020. pp. 367-374
Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms.
Externí odkaz:
http://arxiv.org/abs/2001.07139
Publikováno v:
Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. 2020. pp. 289-305
Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedic
Externí odkaz:
http://arxiv.org/abs/1905.11391
Publikováno v:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019. pp. 1487-1492
Human phenotype-gene relations are fundamental to fully understand the origin of some phenotypic abnormalities and their associated diseases. Biomedical literature is the most comprehensive source of these relations, however, we need Relation Extract
Externí odkaz:
http://arxiv.org/abs/1903.10728
Autor:
Ruas, Pedro, Couto, Francisco M.
Publikováno v:
In Journal of Biomedical Informatics August 2022 132
