causalizeR: a text mining algorithm to identify causal relationships in scientific literature
Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using dat...
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ftdoajarticles:oai:doaj.org/article:ec3cde296608410695af8b094ed89bff 2024-01-07T09:47:07+01:00 causalizeR: a text mining algorithm to identify causal relationships in scientific literature Francisco J. Ancin-Murguzur Vera H. Hausner 2021-07-01T00:00:00Z https://doi.org/10.7717/peerj.11850 https://doaj.org/article/ec3cde296608410695af8b094ed89bff EN eng PeerJ Inc. https://peerj.com/articles/11850.pdf https://peerj.com/articles/11850/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.11850 2167-8359 https://doaj.org/article/ec3cde296608410695af8b094ed89bff PeerJ, Vol 9, p e11850 (2021) Big data Evidence synthesis Scenarios Natural language processing Literature review Medicine R Biology (General) QH301-705.5 article 2021 ftdoajarticles https://doi.org/10.7717/peerj.11850 2023-12-10T01:53:36Z Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR (https://github.com/fjmurguzur/causalizeR), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem. Article in Journal/Newspaper Tundra Directory of Open Access Journals: DOAJ Articles PeerJ 9 e11850 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Big data Evidence synthesis Scenarios Natural language processing Literature review Medicine R Biology (General) QH301-705.5 |
spellingShingle |
Big data Evidence synthesis Scenarios Natural language processing Literature review Medicine R Biology (General) QH301-705.5 Francisco J. Ancin-Murguzur Vera H. Hausner causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
topic_facet |
Big data Evidence synthesis Scenarios Natural language processing Literature review Medicine R Biology (General) QH301-705.5 |
description |
Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR (https://github.com/fjmurguzur/causalizeR), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem. |
format |
Article in Journal/Newspaper |
author |
Francisco J. Ancin-Murguzur Vera H. Hausner |
author_facet |
Francisco J. Ancin-Murguzur Vera H. Hausner |
author_sort |
Francisco J. Ancin-Murguzur |
title |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_short |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_full |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_fullStr |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_full_unstemmed |
causalizeR: a text mining algorithm to identify causal relationships in scientific literature |
title_sort |
causalizer: a text mining algorithm to identify causal relationships in scientific literature |
publisher |
PeerJ Inc. |
publishDate |
2021 |
url |
https://doi.org/10.7717/peerj.11850 https://doaj.org/article/ec3cde296608410695af8b094ed89bff |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
PeerJ, Vol 9, p e11850 (2021) |
op_relation |
https://peerj.com/articles/11850.pdf https://peerj.com/articles/11850/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.11850 2167-8359 https://doaj.org/article/ec3cde296608410695af8b094ed89bff |
op_doi |
https://doi.org/10.7717/peerj.11850 |
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PeerJ |
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9 |
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e11850 |
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