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Bridging Research Fields : An Empirical Study on Joint, Neural Relation Extraction Techniques

Title data

Ackermann, Lars ; Neuberger, Julian ; Käppel, Martin ; Jablonski, Stefan:
Bridging Research Fields : An Empirical Study on Joint, Neural Relation Extraction Techniques.
In: Indulska, Marta ; Reinhartz-Berger, Iris ; Cetina, Carlos ; Pastor, Oscar (ed.): Advanced Information Systems Engineering : proceedings. - Cham : Springer , 2023 . - pp. 471-486 . - (Lecture Notes in Computer Science ; 13901 )
ISBN 978-3-031-34560-9
DOI: https://doi.org/10.1007/978-3-031-34560-9_28

Official URL: Volltext

Project information

Project title:
Project's official title
Project's id
InfoFormulizer
AZ-1390-19

Project financing: Bayerische Forschungsstiftung

Abstract in another language

Information systems that have to deal with natural language text are often equipped with application-specific techniques for solving various Natural Language Processing (NLP) tasks. One of those tasks, extracting entities and their relations from human-readable text, is relevant for downstream tasks like automated model extraction (e.g. UML diagrams, business process models) and question answering (e.g. in chatbots). In NLP the rapidly evolving research field of Relation Extraction denotes a family of techniques for solving this task application-independently. Thus, the question arises why scientific publications about information systems often neglect those existing solutions. One supposed reason is that for reliably selecting an appropriate technique, a comprehensive study of the available alternatives is required. However, existing studies (i) cannot be considered complete due to irreproducible literature search methods and (ii) lack validity, since they compare relevant approaches based on different datasets and different experimental setups. This paper presents an empirical comparative study on domain-independent, open-source deep learning techniques for extracting entities and their relations jointly from texts. Limitations of former studies are overcome (i) by a rigorous and well-documented literature search and (ii) by evaluating relevant techniques on equal datasets in a unified experimental setup. The results1 show that a group of approaches form a reliable baseline for developing new techniques or for utilizing them directly in the above mentioned application scenarios.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Named Entity Recognition; Relation Extraction; Natural Language Processing; Artificial Intelligence
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski
Faculties
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works
000 Computer Science, information, general works > 004 Computer science
Date Deposited: 18 Jul 2023 07:27
Last Modified: 09 Aug 2023 10:43
URI: https://eref.uni-bayreuth.de/id/eprint/86141