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OpBerg: Discovering Causal Sentences Using Optimal Alignments

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Big Data Analytics and Knowledge Discovery (DaWaK 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13428))

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Abstract

The biological literature is rich with sentences that describe causal relations. Methods that automatically extract such sentences can help biologists to synthesize the literature and even discover latent relations that had not been articulated explicitly. Current methods for extracting causal sentences are based on either machine learning or a predefined database of causal terms. Machine learning approaches require a large set of labeled training data and can be susceptible to noise. Methods based on predefined databases are limited by the quality of their curation and are unable to capture new concepts or mistakes in the input. We address these challenges by adapting and improving a method designed for a seemingly unrelated problem: finding alignments between genomic sequences. This paper presents a novel method for extracting causal relations from text by aligning the part-of-speech representations of an input set with that of known causal sentences. Our experiments show that when applied to the task of finding causal sentences in biological literature, our method improves on the accuracy of other methods in a computationally efficient manner.

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Author information

Authors and Affiliations

  1. University of California, Los Angeles, CA, 90095, USA

    Justin Wood, Nicholas Matiasz, Alcino Silva, William Hsu & Wei Wang

  2. Mayo Clinic, 200 First Street, Rochester, MN, 55905, USA

    Alexej Abyzov

Authors
  1. Justin Wood
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  2. Nicholas Matiasz
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  3. Alcino Silva
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  4. William Hsu
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  5. Alexej Abyzov
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  6. Wei Wang
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Corresponding author

Correspondence to Justin Wood .

Editor information

Editors and Affiliations

  1. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  2. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Johann Gamper

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Wood, J., Matiasz, N., Silva, A., Hsu, W., Abyzov, A., Wang, W. (2022). OpBerg: Discovering Causal Sentences Using Optimal Alignments. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2022. Lecture Notes in Computer Science, vol 13428. Springer, Cham. https://doi.org/10.1007/978-3-031-12670-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-12670-3_2

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  • Online ISBN: 978-3-031-12670-3

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