Home > Research > Publications & Outputs > Detecting document structure in a very large co...

Electronic data

  • elhaj_lrec_2014

    Rights statement: The LREC 2014 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

    Final published version, 249 KB, PDF-document

    Available under license: CC BY-NC

Links

View graph of relations

Detecting document structure in a very large corpus of UK financial reports

Research output: Contribution in Book/Report/ProceedingsPaper

Published
Publication date2014
Host publicationLREC'14 Ninth International Conference on Language Resources and Evaluation
Place of PublicationReykjavik, Iceland
PublisherEuropean Language Resources Association (ELRA)
Pages1335-1338
Number of pages4
ISBN (Print)9782951740884
<mark>Original language</mark>English

Publication series

NameProceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014)
PublisherEuropean Language Resources Association (ELRA)

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 based on full texts.