Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

Sedinkina, Marina; Breitkopf, Nikolas und Schütze, Hinrich (28. Juli 2019): Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes. , July 28 - August 2, 2019, Florence, Italy

Abstract

In this paper, we automatically create senti- ment dictionaries for predicting financial out- comes. We compare three approaches: (i) manual adaptation of the domain-general dic- tionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first man- ual, then automatic adaptation. In our experi- ments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the finan- cial outcomes excess return and volatility. In particular, automatic adaptation performs bet- ter 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 do- main instead.

• Dokumententyp: Konferenz
• EU Funded Grant Agreement Number: 740516
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement
• Fakultätsübergreifende Einrichtungen: Centrum für Informations- und Sprachverarbeitung (CIS)
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 400 Sprache > 410 Linguistik
• ISBN: 978-1-950737-48-2
• Ort: Stroudsburg, USA
• Sprache: Englisch