Efficient Statement Identification for Automatic Market Forecasting

verfasst von

Henning Wachsmuth, Peter Prettenhofer, Benno Stein

Abstract

Strategic business decision making involves the analysis of market forecasts. Today, the identification and aggregation of relevant market statements is done by human experts, often by analyzing documents from the World Wide Web. We present an efficient information extraction chain to automate this complex natural language processing task and show results for the identification part. Based on time and money extraction, we identify sentences that represent statements on revenue using support vector classification. We provide a corpus with German online news articles, in which more than 2,000 such sentences are annotated by domain experts from the industry. On the test data, our statement identification algorithm achieves an overall precision and recall of 0.86 and 0.87 respectively.

Externe Organisation(en)

Universität Paderborn
Bauhaus-Universität Weimar

Typ

Aufsatz in Konferenzband

Seiten

1128-1136

Anzahl der Seiten

9

Publikationsdatum

2010

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Sprache und Linguistik, Theoretische Informatik und Mathematik, Linguistik und Sprache

Elektronische Version(en)

https://aclanthology.org/C10-1127 (Zugang: Offen)