Automatic detection of speculation in policy statements

Štajner, Sanja ; Baerg, Nicole ; Ponzetto, Simone Paolo ; Stuckenschmidt, Heiner

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Document Type: Conference or workshop publication
Year of publication: 2016
Book title: NLP+CSS workshop at Web Science 2016 : May 22, 2016, Hannover, Germany : accepted papers
Page range: 1-5
Conference title: NLP+CSS Workshop at Web Science 2016
Location of the conference venue: Hannover, Germany
Date of the conference: May 22, 2016
Place of publication: New York, NY
Publishing house: ACM
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Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Keywords (English): speculation detection , monetary policy statements , text classification
Abstract: In this paper, we present the first study of automatic detection of speculative sentences in official monetary policy statements. We build two expert-annotated datasets. The first contains the transcripts of monetary policy meetings on the U.S. central bank’s monetary policy committee (Debates). The second contains the official monetary policy statements (Decisions). We use the first part of the Debates dataset to build dictionaries with lexical triggers for speculative and non-speculative sentences. We then test their performance on an in-domain test set (the second part of the same dataset) and on an out-of-domain test set (the Decisions dataset) using several rule-based and machine learning classifiers. Our best classifiers achieve an accuracy of 82.5% (0.70 F-score on the speculative class), comparable with automatic detection of speculative sentences in Wikipedia articles.
Additional information: Online-Ressource

Dieser Eintrag ist Teil der Universitätsbibliographie.

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