BAMBUROVÁ, Michaela and Zuzana NEVĚŘILOVÁ. Structured Information Extraction from Pharmaceutical Records. In Aleš Horák, Pavel Rychlý, Adam Rambousek. Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2019. Brno: Tribun EU, 2019, p. 55-62. ISBN 978-80-263-1530-8.
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Basic information
Original name Structured Information Extraction from Pharmaceutical Records
Authors BAMBUROVÁ, Michaela (703 Slovakia, belonging to the institution) and Zuzana NEVĚŘILOVÁ (203 Czech Republic, belonging to the institution).
Edition Brno, Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2019, p. 55-62, 8 pp. 2019.
Publisher Tribun EU
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10200 1.2 Computer and information sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14330/19:00111627
Organization unit Faculty of Informatics
ISBN 978-80-263-1530-8
ISSN 2336-4289
UT WoS 000604899800007
Keywords in English structured information extraction; table understanding; entity recognition
Changed by Changed by: Mgr. Michal Petr, učo 65024. Changed: 16/5/2022 15:23.
Abstract
The paper presents an iterative approach to understanding semi-structured or unstructured tabular data with pharmaceutical records. Thetask is to split records with entities such as drug name, dosage strength,dosage form, and package size into the appropriate columns. The data isprovided by many suppliers, and so it is very diverse in terms of structure.Some of the records are easy to parse using regular expressions; othersare difficult and need advanced methods. We used regular expressionsfor the easy-to-parse data and conditional random fields for the morecomplex records. We iteratively extend the training data set using theabove methods together with manual corrections. Currently, the F1 scorefor correct classification into 5 classes is 95%.
Links
EF16_013/0001781, research and development projectName: LINDAT/CLARIN - Výzkumná infrastruktura pro jazykové technologie - rozšíření repozitáře a výpočetní kapacity
LM2015071, research and development projectName: Jazyková výzkumná infrastruktura v České republice (Acronym: LINDAT-Clarin)
Investor: Ministry of Education, Youth and Sports of the CR
PrintDisplayed: 30/8/2024 16:24