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Fact-Aware Document Retrieval for Information Extraction

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Abstract

Exploiting textual information from large document collections such as the Web with structured queries is an often requested, but still unsolved requirement of many users. We present BlueFact, a framework for efficiently retrieving documents containing structured, factual information from a full-text index. This is an essential building block for information extraction systems that enable ad-hoc analytical queries on unstructured text data as well as knowledge harvesting in a digital archive scenario.

Our approach is based on the observation that documents share a set of common grammatical structures and words for expressing facts. Our system observes these keyword phrases using structural, syntactic, lexical and semantic features in an iterative, cost effective training process and systematically queries the search engine index with these automatically generated phrases. Next, BlueFact retrieves a list of document identifiers, combines observed keywords as evidence for a factual information and infers the relevance for each document identifier. Finally, we forward the documents in the order of their estimated relevance to an information extraction service. That way BlueFact can efficiently retrieve all the structured, factual information contained in an indexed collection of text documents.

We report results of a comprehensive experimental evaluation over 20 different fact types on the Reuters News Corpus Volume I (RCV1). BlueFact’s scoring model and feature generation methods significantly outperform existing approaches in terms of fact retrieval performance. BlueFact fires significantly fewer queries against the index, requires significantly less execution time and achieves very high fact recall across different domains.

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Notes

  1. The average execution time for an average sized document in the Reuters news corpus with a size of 1.4 Kbyte was 1.3 seconds. The average extraction time for the larges document in the corpus (52.45 KB) was 28 seconds with the OpenCalais.com information extraction service we used.

  2. Reuters Corpus Volume I (RCV1) available at http://trec.nist.gov/data/reuters/reuters.html.

  3. Noun phrase.

  4. http://lucene.apache.org/.

  5. http://hsqldb.org/.

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Acknowledgements

The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. FP7-ICT-2009-5-257859, ‘Risk and Opportunity management of huge-scale BUSiness community cooperation’ (ROBUST). Alexander Löser also receives funding from the Federal Ministry of Economics and Technology (BMWi) under grant agreement “01MD11014A, ‘MIA-Marktplatz für Informationen und Analysen’ (MIA)”.

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Authors and Affiliations

  1. University of Technology Berlin, Einsteinufer 17, 10587, Berlin, Germany

    Christoph Boden, Alexander Löser, Christoph Nagel & Stephan Pieper

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  1. Christoph Boden
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  2. Alexander Löser
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  4. Stephan Pieper
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Correspondence to Christoph Boden.

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Boden, C., Löser, A., Nagel, C. et al. Fact-Aware Document Retrieval for Information Extraction. Datenbank Spektrum 12, 89–100 (2012). https://doi.org/10.1007/s13222-012-0088-4

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