Abstract
Financial news carry information about economical figures and indicators. However, these texts are mostly unstructured and consequently hard to be processed in an automatic way. In this paper, we present a representation formalism that supports a linguistic composition for machine learning tasks. We show an innovative approach to structuring financial texts by extracting principal indicators. Considering announcements in the monetary policy domain, we distinguish between attributes and their values and argue that attributes are to be represented as an aggregated set of economic terms, keeping their values as corresponding conditional expressions. We close with a critical discussion and future perspectives.
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Minev, M., Schommer, C. (2014). Domain-Driven News Representation Using Conditional Attribute-Value Pairs. In: Ferro, N. (eds) Bridging Between Information Retrieval and Databases. PROMISE 2013. Lecture Notes in Computer Science, vol 8173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54798-0_11
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DOI: https://doi.org/10.1007/978-3-642-54798-0_11
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