- Improving spaCy dependency annotation and PoS tagging web service using independent NER services
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Colic N, Rinaldi F
- KMID: 2462031
- Genomics Inform.
- 2019 Jun;17(2):e21.
- doi: 10.5808/GI.2019.17.2.e21
Dependency parsing is often used as a component in many text analysis pipelines. However, performance, especially in specialized domains, suffers from the presence of complex terminology. Our hypothesis is that... -
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- Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data
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Ferré A, Ba M, Bossy R
- KMID: 2462030
- Genomics Inform.
- 2019 Jun;17(2):e20.
- doi: 10.5808/GI.2019.17.2.e20
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of... -
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- Towards cross-platform interoperability for machine-assisted text annotation
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Eckart de Castilho R, Ide N, Kim JD, Klie JC, Suderman K
- KMID: 2462029
- Genomics Inform.
- 2019 Jun;17(2):e19.
- doi: 10.5808/GI.2019.17.2.e19
In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models... -
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- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition
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Gachloo M, Wang Y, Xia J
- KMID: 2462028
- Genomics Inform.
- 2019 Jun;17(2):e18.
- doi: 10.5808/GI.2019.17.2.e18
Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the... -
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- OryzaGP: rice gene and protein dataset for named-entity recognition
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Larmande P, Do H, Wang Y
- KMID: 2462027
- Genomics Inform.
- 2019 Jun;17(2):e17.
- doi: 10.5808/GI.2019.17.2.e17
Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific... -
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- Resources for assigning MeSH IDs to Japanese medical terms
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Tateisi Y
- KMID: 2462026
- Genomics Inform.
- 2019 Jun;17(2):e16.
- doi: 10.5808/GI.2019.17.2.e16
Medical Subject Headings (MeSH), a medical thesaurus created by the National Library of Medicine (NLM), is a useful resource for natural language processing (NLP). In this article, the current status... -
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- PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts
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Armengol-Estapé J, Soares F, Marimon M, Krallinger M
- KMID: 2462025
- Genomics Inform.
- 2019 Jun;17(2):e15.
- doi: 10.5808/GI.2019.17.2.e15
Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions... -
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- Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications
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Garcia L, Giraldo O, Garcia A, Rebholz-Schuhmann D
- KMID: 2462024
- Genomics Inform.
- 2019 Jun;17(2):e14.
- doi: 10.5808/GI.2019.17.2.e14
The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata... -
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- Fully connecting the Observational Health Data Science and Informatics (OHDSI) initiative with the world of linked open data
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Banda JM
- KMID: 2462023
- Genomics Inform.
- 2019 Jun;17(2):e13.
- doi: 10.5808/GI.2019.17.2.e13
The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics... -
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- Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining
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Kim JD, Cohen KB, Collier N, Lu Z, Rinaldi F
- KMID: 2462022
- Genomics Inform.
- 2019 Jun;17(2):e12.
- doi: 10.5808/GI.2019.17.2.e12
No abstract available. -
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