Abstract
Automatically extracting different types of knowledge from authoritative biomedical texts, e.g., scientific medical literature, electronic health records etc., and representing it in a computer analyzable as well as human-readable form is an important but challenging task. One such knowledge is identifying entities with their biological types in the biomedical domain.
In this paper, we propose a system which extracts end-to-end entity mentions with their biological types from a sentence. We consider 7 interrelated tags for biological types viz., gene, biological-process, molecular-function, cellular-component, protein, disease, drug. Our system employs an automatically created biological ontology and implements an efficient matching algorithm for end-to-end entity extraction. We compare our approach with a Noun-based entity extraction system (baseline) as well as we show a significant improvement over standard entity extraction tools, viz., Stanford-NER, Stanford-OpenIE.
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Notes
- 1.
Available at: https://nlp.stanford.edu/software/CRF-NER.shtml.
- 2.
Available at: https://nlp.stanford.edu/software/openie.html.
- 3.
- 4.
We also experimented with Stanford POS tagger, but the performance of this tagger was worse than NLTK tagger for the biological entities.
- 5.
- 6.
Though we obtained subsequences up-to length (n), we observed that there were no entity more than 4 words long.
- 7.
Available at: http://www.nltk.org/_modules/nltk/model/ngram.html.
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Sangwan, S., Sharma, R., Palshikar, G., Ekbal, A. (2023). Cold Is a Disease and D-cold Is a Drug: Identifying Biological Types of Entities in the Biomedical Domain. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_5
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