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Information extraction as a filtering task

Authors:

Henning Wachsmuth,

Gregor EngelsAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 2049 - 2058

https://doi.org/10.1145/2505515.2505557

Published: 27 October 2013 Publication History

Abstract

Information extraction is usually approached as an annotation task: Input texts run through several analysis steps of an extraction process in which different semantic concepts are annotated and matched against the slots of templates. We argue that such an approach lacks an efficient control of the input of the analysis steps. In this paper, we hence propose and evaluate a model and a formal approach that consistently put the filtering view in the focus: Before spending annotation effort, filter those portions of the input texts that may contain relevant information for filling a template and discard the others. We model all dependencies between the semantic concepts sought for with a truth maintenance system, which then efficiently infers the portions of text to be annotated in each analysis step. The filtering view enables an information extraction system (1) to annotate only relevant portions of input texts and (2) to easily trade its run-time efficiency for its recall. We provide our approach as an open-source extension of Apache UIMA and we show the potential of our approach in a number of experiments.

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Cited By

Aquino IM. dos Santos MDorneles CT. Carvalho J(2024)Extracting Information from Brazilian Legal Documents with Retrieval Augmented GenerationAnais Estendidos do XXXIX Simpósio Brasileiro de Banco de Dados (SBBD Estendido 2024)10.5753/sbbd_estendido.2024.244241(280-287)Online publication date: 14-Oct-2024
https://doi.org/10.5753/sbbd_estendido.2024.244241

Index Terms

Information extraction as a filtering task
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Theory of computation
  1. Computational complexity and cryptography
    1. Complexity classes

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Information

Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013.

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Publication History

Published: 27 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

Sponsor:
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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

Aquino IM. dos Santos MDorneles CT. Carvalho J(2024)Extracting Information from Brazilian Legal Documents with Retrieval Augmented GenerationAnais Estendidos do XXXIX Simpósio Brasileiro de Banco de Dados (SBBD Estendido 2024)10.5753/sbbd_estendido.2024.244241(280-287)Online publication date: 14-Oct-2024
https://doi.org/10.5753/sbbd_estendido.2024.244241

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