Information Extraction as a Filtering Task

verfasst von

Henning Wachsmuth, Benno Stein, Gregor Engels

Abstract

Information extraction is usually approached as an annotation task: Input texts run through several analysis steps of an extraction process in which different semantic concepts are annotated and matched against the slots of templates. We argue that such an approach lacks an efficient control of the input of the analysis steps. In this paper, we hence propose and evaluate a model and a formal approach that consistently put the filtering view in the focus: Before spending annotation effort, filter those portions of the input texts that may contain relevant information for filling a template and discard the others. We model all dependencies between the semantic concepts sought for with a truth maintenance system, which then efficiently infers the portions of text to be annotated in each analysis step. The filtering view enables an information extraction system (1) to annotate only relevant portions of input texts and (2) to easily trade its run-time efficiency for its recall. We provide our approach as an open-source extension of Apache UIMA and we show the potential of our approach in a number of experiments. Copyright is held by the owner/author(s).

Externe Organisation(en)

Universität Paderborn
Bauhaus-Universität Weimar

Typ

Aufsatz in Konferenzband

Seiten

2049-2058

Anzahl der Seiten

10

Publikationsdatum

27.10.2013

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Entscheidungswissenschaften (insg.), Betriebswirtschaft, Management und Rechnungswesen (insg.)

Elektronische Version(en)

https://doi.org/10.1145/2505515.2505557 (Zugang: Geschlossen)