Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

Sedinkina, Marina; Breitkopf, Nikolas und Schuetze, Hinrich (2019): Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes. In: 57Th Annual Meeting of the Association for Computational Linguistics (Acl 2019): S. 346-359

Abstract

In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert's a priori belief about a word's meaning can be incorrect - annotation should be performed based on the word's contexts in the target domain instead.

• Dokumententyp: Zeitschriftenartikel
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 400 Sprache > 400 Sprache
• Sprache: Englisch