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.
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Dieser Eintrag ist Teil der Universitätsbibliographie.
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