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        <title>Detecting Document Structure in a Very Large Corpus of UK Financial Reports</title>
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        <namePart type="given">Mahmoud</namePart>
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    <abstract>In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports. The work presented is part of the Corporate Financial Information Environment (CFIE) project in which we are using Natural Language Processing (NLP) techniques to study the causes and consequences of corporate disclosure and financial reporting outcomes. We aim to uncover the determinants of financial reporting quality and the factors that influence the quality of information disclosed to investors beyond the financial statements. The CFIE consists of the supply of information by firms to investors, and the mediating influences of information intermediaries on the timing, relevance and reliability of information available to investors. It is important to compare and contrast specific elements or sections of each annual financial report across our entire corpus rather than working at the full document level. We show that the values of some metrics e.g. readability will vary across sections, thus improving on previous research research based on full texts.</abstract>
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%0 Conference Proceedings
%T Detecting Document Structure in a Very Large Corpus of UK Financial Reports
%A El-Haj, Mahmoud
%A Rayson, Paul
%A Young, Steve
%A Walker, Martin
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 may
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F el-haj-etal-2014-detecting
%X In this paper we present the evaluation of our automatic methods for detecting and extracting document structure in annual financial reports. The work presented is part of the Corporate Financial Information Environment (CFIE) project in which we are using Natural Language Processing (NLP) techniques to study the causes and consequences of corporate disclosure and financial reporting outcomes. We aim to uncover the determinants of financial reporting quality and the factors that influence the quality of information disclosed to investors beyond the financial statements. The CFIE consists of the supply of information by firms to investors, and the mediating influences of information intermediaries on the timing, relevance and reliability of information available to investors. It is important to compare and contrast specific elements or sections of each annual financial report across our entire corpus rather than working at the full document level. We show that the values of some metrics e.g. readability will vary across sections, thus improving on previous research research based on full texts.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/402_Paper.pdf
%P 1335-1338

Markdown (Informal)

[Detecting Document Structure in a Very Large Corpus of UK Financial Reports](http://www.lrec-conf.org/proceedings/lrec2014/pdf/402_Paper.pdf) (El-Haj et al., LREC 2014)

• Detecting Document Structure in a Very Large Corpus of UK Financial Reports (El-Haj et al., LREC 2014)

ACL

• Mahmoud El-Haj, Paul Rayson, Steve Young, and Martin Walker. 2014. Detecting Document Structure in a Very Large Corpus of UK Financial Reports. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1335–1338, Reykjavik, Iceland. European Language Resources Association (ELRA).