A General-Purpose Annotation Model for Knowledge Discovery: Case Study in Spanish Clinical Text

Alejandro Piad-Morffis, Yoan Guitérrez, Suilan Estevez-Velarde, Rafael Muñoz


Abstract
Knowledge discovery from text in natural language is a task usually aided by the manual construction of annotated corpora. Specifically in the clinical domain, several annotation models are used depending on the characteristics of the task to solve (e.g., named entity recognition, relation extraction, etc.). However, few general-purpose annotation models exist, that can support a broad range of knowledge extraction tasks. This paper presents an annotation model designed to capture a large portion of the semantics of natural language text. The structure of the annotation model is presented, with examples of annotated sentences and a brief description of each semantic role and relation defined. This research focuses on an application to clinical texts in the Spanish language. Nevertheless, the presented annotation model is extensible to other domains and languages. An example of annotated sentences, guidelines, and suitable configuration files for an annotation tool are also provided for the research community.
Anthology ID:
W19-1910
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–88
Language:
URL:
https://aclanthology.org/W19-1910
DOI:
10.18653/v1/W19-1910
Bibkey:
Cite (ACL):
Alejandro Piad-Morffis, Yoan Guitérrez, Suilan Estevez-Velarde, and Rafael Muñoz. 2019. A General-Purpose Annotation Model for Knowledge Discovery: Case Study in Spanish Clinical Text. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 79–88, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
A General-Purpose Annotation Model for Knowledge Discovery: Case Study in Spanish Clinical Text (Piad-Morffis et al., ClinicalNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W19-1910.pdf
Data
BioFrameNet