loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Daniel Schuster ; Daniel Esser ; Klemens Muthmann and Alexander Schill

Affiliation: Technische Universität Dresden, Germany

Keyword(s): Self-learning Information Extraction, Cooperative Extraction, Document Archiving, Business Documents.

Related Ontology Subjects/Areas/Topics: Collaborative Computing ; Coupling and Integrating Heterogeneous Data Sources ; Databases and Information Systems Integration ; Distributed Database Systems ; Enterprise Information Systems ; Software Agents and Internet Computing

Abstract: Business document indexing for ordered filing of documents is a crucial task for every company. Since this is a tedious error prone work, automatic or at least semi-automatic approaches have a high value. One approach for semi-automated indexing of business documents uses self-learning information extraction methods based on user feedback. While these methods require no management of complex indexing rules, learning by user feedback requires each user to first provide a number of correct extractions before getting appropriate automatic results. To eliminate this cold start problem we propose a cooperative approach to document information extraction involving dynamic hierarchies of extraction services. We provide strategies for making the decision when to contact another information extraction service within the hierarchy, methods to combine results from different sources, as well as aging and split strategies to reduce the size of cooperatively used indexes. An evaluation with a larg e number of real-world business documents shows the benefits of our approach. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.118.119.77

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Schuster, D.; Esser, D.; Muthmann, K. and Schill, A. (2015). Modelspace - Cooperative Document Information Extraction in Flexible Hierarchies. In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-096-3; ISSN 2184-4992, SciTePress, pages 321-329. DOI: 10.5220/0005376403210329

@conference{iceis15,
author={Daniel Schuster. and Daniel Esser. and Klemens Muthmann. and Alexander Schill.},
title={Modelspace - Cooperative Document Information Extraction in Flexible Hierarchies},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2015},
pages={321-329},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005376403210329},
isbn={978-989-758-096-3},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Modelspace - Cooperative Document Information Extraction in Flexible Hierarchies
SN - 978-989-758-096-3
IS - 2184-4992
AU - Schuster, D.
AU - Esser, D.
AU - Muthmann, K.
AU - Schill, A.
PY - 2015
SP - 321
EP - 329
DO - 10.5220/0005376403210329
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic