Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/36608 
Year of Publication: 
2008
Series/Report no.: 
Technical Report No. 2008,04
Publisher: 
Technische Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Abstract: 
Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.
Document Type: 
Working Paper

Files in This Item:
File
Size
265.84 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.