Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autor(en): Evert S, Heinrich P, Henselmann K, Rabenstein U, Scherr E, Schröder L
Herausgeber: Loukanova R, Liefke K
Verlag: Stockholm University
Verlagsort: Stockholm
Jahr der Veröffentlichung: 2017
Tagungsband: Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing2017)
Seitenbereich: 47 - 62
Sprache: Englisch


Abstract


We investigate an approach of improving statistical text classification by combining machine learners with an ontology-based identification of domain-specific topic categories. We apply this approach to ad hoc disclosures by public companies. This form of obligatory publicity concerns all information that might affect the stock price; relevant topic categories are governed by stringent regulations. Our goal is to classify disclosures according to their effect on stock prices (negative, neutral, positive). In the feasibility study reported here, we combine natural language parsing with a formal background ontology to recognize disclosures concerning a particular topic, viz. retirement of key personnel. The semantic analysis identifies such disclosures with high precision and recall. We then demonstrate that machine learners benefit from the additional ontology-based information in different prediction tasks.



FAU-Autoren / FAU-Herausgeber

Evert, Stefan Prof. Dr.
Lehrstuhl für Korpus- und Computerlinguistik
Heinrich, Philipp
Lehrstuhl für Korpus- und Computerlinguistik
Henselmann, Klaus Prof. Dr.
Lehrstuhl für Betriebswirtschaftslehre, insbesondere Rechnungswesen und Prüfungswesen
Rabenstein, Ulrich
Lehrstuhl für Informatik 8 (Theoretische Informatik)
Scherr, Elisabeth
Juniorprofessur für Wirtschaftsprüfung mit dem Schwerpunkt digitale Datenanalyse
Schröder, Lutz Prof. Dr.
Lehrstuhl für Informatik 8 (Theoretische Informatik)


Zitierweisen

APA:
Evert, S., Heinrich, P., Henselmann, K., Rabenstein, U., Scherr, E., & Schröder, L. (2017). Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures. In Loukanova R, Liefke K (Eds.), Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing2017) (pp. 47 - 62). Stockholm, SE: Stockholm: Stockholm University.

MLA:
Evert, Stefan, et al. "Combining Machine Learning and Semantic Features in the Classification of Corporate Disclosures." Proceedings of the Logic and Algorithms in Computational Linguistics 2017 (LACompLing2017), Stockholm Ed. Loukanova R, Liefke K, Stockholm: Stockholm University, 2017. 47 - 62.

BibTeX: 

Zuletzt aktualisiert 2018-11-08 um 00:09