Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts
We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and...
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| Veröffentlicht in: | IEEE/ACM transactions on computational biology and bioinformatics 2020-11, Vol.17 (6), p.1895-1906 |
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| container_title | IEEE/ACM transactions on computational biology and bioinformatics |
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| creator | Noriega-Atala, Enrique Hein, Paul D. Thumsi, Shraddha S. Wong, Zechy Wang, Xia Hendryx, Sean M. Morrison, Clayton T. |
| description | We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study. |
| doi_str_mv | 10.1109/TCBB.2019.2904231 |
| format | Article |
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This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. 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| subjects | Bioinformatics Biological information theory Classifiers Context Context awareness Data mining Feature extraction Impact analysis inter-sentence relation extraction Knowledge based systems Linguistics NLP Texts |
| title | Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts |
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