Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Jan-Christoph Klie"'
Publikováno v:
Computational Linguistics, Vol 49, Iss 1 (2023)
Annotated data is an essential ingredient in natural language processing for training and evaluating machine learning models. It is therefore very desirable for the annotations to be of high quality. Recent work, however, has shown that several popul
Externí odkaz:
https://doaj.org/article/c2208525153f468a9148bd1a812b3032
Publikováno v:
Computational Linguistics, Vol 48, Iss 4 (2022)
The authors of this work (“Annotation Curricula to Implicitly Train Non-Expert Annotators” by Ji-Ung Lee, Jan-Christoph Klie, and Iryna Gurevych in Computational Linguistics 48:2 https://doi.org/10.1162/coli_a_00436) discovered an incorrect inequ
Externí odkaz:
https://doaj.org/article/c0ba1a2094d947249336e959488c3bc2
Publikováno v:
Computational Linguistics, Vol 48, Iss 2 (2022)
Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain. This can be overwhelming in the beginning, mentally taxing, and induce errors into the resulting annotations; especially
Externí odkaz:
https://doaj.org/article/5b25a81bccc24a7e917d571292f34883
Publikováno v:
Genomics & Informatics, Vol 17, Iss 2 (2019)
In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are pa
Externí odkaz:
https://doaj.org/article/5d51f3e4eee74d9fa6cb0c33d56b9945
Publikováno v:
Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances.
Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). To quickly annotate texts with EL even in low-resource domains and noisy text, we present a novel Human-In-The-Loop EL approach. We show that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8140256eba969b95135981a9452d63f2
https://doi.org/10.18653/v1/2021.dash-1.6
https://doi.org/10.18653/v1/2021.dash-1.6
Publikováno v:
ACL
Entity linking (EL) is concerned with disambiguating entity mentions in a text against knowledge bases (KB). It is crucial in a considerable number of fields like humanities, technical writing and biomedical sciences to enrich texts with semantics an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::752292a2a6896c72970764bf6cd69616
https://doi.org/10.18653/v1/2020.acl-main.624
https://doi.org/10.18653/v1/2020.acl-main.624
Publikováno v:
LAW@ACL
This paper describes an ecosystem consisting of three independent text annotation platforms. To demonstrate their ability to work in concert, we illustrate how to use them to address an interactive domain adaptation task in biomedical entity recognit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc21453a166624eb7efe93b69addf9fa
https://doi.org/10.18653/v1/w19-4021
https://doi.org/10.18653/v1/w19-4021
Publikováno v:
eScience
In the Digital Humanities (DH), linking text collections to general or domain-specific knowledge bases (KBs) or authority files is important to enable a contextualised analysis. Automatic named entity recognition and entity linking tools require trai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42e7cb521a7ecb7492d0ffefa78d7bc1
https://doi.org/10.1109/escience.2018.00077
https://doi.org/10.1109/escience.2018.00077
Publikováno v:
EMNLP (Demonstration)
Annotating entity mentions and linking them to a knowledge resource are essential tasks in many domains. It disambiguates mentions, introduces cross-document coreferences, and the resources contribute extra information, e.g. taxonomic relations. Such
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45e2e201b6acfd935d981993cf2592b9
https://doi.org/10.18653/v1/d18-2022
https://doi.org/10.18653/v1/d18-2022
Publikováno v:
NAACL-HLT
An essential step in FrameNet Semantic Role Labeling is the Frame Identification (FrameId) task, which aims at disambiguating a situation around a predicate. Whilst current FrameId methods rely on textual representations only, we hypothesize that Fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cedb97dd68bc64ff0d1b9fc68c02a5ae
https://doi.org/10.18653/v1/n18-1134
https://doi.org/10.18653/v1/n18-1134