Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Egoitz Laparra"'
Publikováno v:
Yearbook of Medical Informatics
Summary Objectives: We survey recent work in biomedical NLP on building more adaptable or generalizable models, with a focus on work dealing with electronic health record (EHR) texts, to better understand recent trends in this area and identify oppor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::776b0c2c911d24c2b6863c4aee3b13fe
http://europepmc.org/articles/PMC8416218
http://europepmc.org/articles/PMC8416218
Publikováno v:
JAMIA Open
Building clinical natural language processing (NLP) systems that work on widely varying data is an absolute necessity because of the expense of obtaining new training data. While domain adaptation research can have a positive impact on this problem,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70ebafa41816f810a133b1711bcc1e1e
https://doi.org/10.1093/jamiaopen/ooaa010
https://doi.org/10.1093/jamiaopen/ooaa010
Autor:
Mihai Surdeanu, John Hungerford, Yee Seng Chan, Jessica MacBride, Benjamin Gyori, Andrew Zupon, Zheng Tang, Haoling Qiu, Bonan Min, Yan Zverev, Caitlin Hilverman, Max Thomas, Walter Andrews, Keith Alcock, Zeyu Zhang, Michael Reynolds, Steven Bethard, Rebecca Sharp, Egoitz Laparra
Publikováno v:
Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61798b2a7a417a04e61e29827a5993c8
https://doi.org/10.18653/v1/2022.hcinlp-1.1
https://doi.org/10.18653/v1/2022.hcinlp-1.1
Publikováno v:
SemEval@ACL/IJCNLP
This paper presents the Source-Free Domain Adaptation shared task held within SemEval-2021. The aim of the task was to explore adaptation of machine-learning models in the face of data sharing constraints. Specifically, we consider the scenario where
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a766f972ac48765f45782e54a81615d1
https://doi.org/10.18653/v1/2021.semeval-1.42
https://doi.org/10.18653/v1/2021.semeval-1.42
Publikováno v:
Trans Assoc Comput Linguist
This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8a99069b44dd9fa4a48311c3ffd2b481
https://pubmed.ncbi.nlm.nih.gov/32432133
https://pubmed.ncbi.nlm.nih.gov/32432133
Autor:
Egoitz Laparra, Steven Bethard
Publikováno v:
COLING
Much previous work on geoparsing has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. However, geographical locations occur not only as individual toponyms, but also as compositions of refer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46d69bab2f23ef351b69de3cebdd25d6
https://doi.org/10.18653/v1/2020.coling-main.81
https://doi.org/10.18653/v1/2020.coling-main.81
Publikováno v:
Knowledge-Based Systems. 133:77-89
In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of Multilingual and Cross-lingual data sources. Based on the assumption that event-related information can be recovered fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20be79932fe9152bd80f28a0cc892184
https://doi.org/10.1016/j.knosys.2017.07.002
https://doi.org/10.1016/j.knosys.2017.07.002
Autor:
Steven Bethard, Laura López-Hoffman, Egoitz Laparra, Sophia Wang, Ragheb Al-Ghezi, Yiyun Zhao, Aaron M. Lien
Publikováno v:
LaTeCH@NAACL-HLT
The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years. Unfortunately, there is no central database for this information and it is too vol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a9f3024beacaaa94e5bbe6c940586fb
https://doi.org/10.18653/v1/w19-2506
https://doi.org/10.18653/v1/w19-2506
Autor:
Ajay Nagesh, Zheng Tang, Vikas Yadav, Steven Bethard, Keith Alcock, Egoitz Laparra, Marco Antonio Valenzuela-Escárcega, Benjamin M. Gyori, Fan Luo, Mihai Surdeanu, Clayton T. Morrison, Adarsh Pyarelal, John A. Bachman, Rebecca Sharp, Heather C. Lent, Mithun Paul, Kobus Barnard
Publikováno v:
NAACL-HLT (Demonstrations)
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed appr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc2dd6047d4701c02fd99eb7828d4526
https://doi.org/10.18653/v1/n19-4008
https://doi.org/10.18653/v1/n19-4008
Publikováno v:
SemEval@NAACL-HLT
We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db90ddd8de26884715dc5e5c1a361a00
https://doi.org/10.18653/v1/s19-2232
https://doi.org/10.18653/v1/s19-2232