=Paper=
{{Paper
|id=Vol-1747/BIT102_ICBO2016
|storemode=property
|title=One Tagger, Many Uses: Illustrating the Power of Ontologies in Dictionary-based Named Entity Recognition
|pdfUrl=https://ceur-ws.org/Vol-1747/BIT102_ICBO2016.pdf
|volume=Vol-1747
|authors=Lars Juhl Jensen
|dblpUrl=https://dblp.org/rec/conf/icbo/Jensen16
}}
==One Tagger, Many Uses: Illustrating the Power of Ontologies in Dictionary-based Named Entity Recognition ==
https://ceur-ws.org/Vol-1747/BIT102_ICBO2016.pdf
One tagger, many uses
Illustrating the power of ontologies in dictionary-based named entity recognition
Lars Juhl Jensen
Novo Nordisk Foundation Center for Protein Research
Faculty of Health and Medical Sciences, University of Copenhagen
Copenhagen, Denmark
lars.juhl.jensen@cpr.ku.dk
Abstract— Automatic annotation of text is an important
complement to manual annotation, because the latter is highly III. DICTIONARIES AND APPLICATION AREAS
labour intensive. We have developed a fast dictionary-based Software is only one half of a NER system; the other half is
named entity recognition (NER) system and addressed a wide the dictionary with all the names that the software matches
variety of biomedical problems by applied it to text from many against the text. When adapting the NER system to a new
different sources. We have used this tagger both in real-time biomedical application area, the main work required is the
tools to support curation efforts and in pipelines for populating
construction of a suitable high-quality dictionary and blacklist
databases through bulk processing of entire Medline, the open-
names not to be tagged. The latter is created through manual
access subset of PubMed Central, NIH grant abstracts, FDA
drug labels, electronic health records, and the Encyclopedia of inspection of the most frequently occurring dictionary names in
Life. Despite the simplicity of the approach, it typically achieves a large text corpus.
80–90% precision and 70–80% recall. Many of the underlying
dictionaries were built from open biomedical ontologies, which A. Molecular Entities
further facilitate integration of the text-mining results with NER and normalization of genes and proteins has been the
evidence from other sources. subject of several BioCreative tasks over the years, most
recently in BioCreative III [5]. This was also one of the very
Keywords—named entity recognition; software; dictionaries; first uses of the tagger, which is a key component of the text-
mining pipeline in the STRING database [6]. The underlying
I. INTRODUCTION dictionary of gene/protein names is based on Ensembl [7] and
Named entity recognition (NER) is a fundamental task in RefSeq [8], which were expanded with additional synonyms
biomedical text mining and can benefit greatly from the use of from UniProt [9]. An older version of the system achieved F-
ontologies. This is especially true for dictionary-based NER scores of 91% and 66% for recognition and normalization of
methods, which with a good ontology at hand can be quickly yeast and fly genes, respectively [3]. This NER method is also
adapted to a new task without the need for a manually curated heavily used within the Illuminating the Druggable Genome
corpus for training. program to assess how well studied drug targets are based on
both publications and NIH RePORTER funding data.
II. SOFTWARE IMPLEMENTATION Identification of small-molecule chemical compounds in
The core of our NER system is a highly optimized text was a task in BioCreative V [10]. The tagger is also used
dictionary-based tagging engine implemented in C++ [1]. The for this in the STITCH database [11], which relies on a
core tagger makes use of a custom hashing function to process dictionary constructed from a filtered version of PubChem
thousands of PubMed abstracts per second with a single CPU [12]. STRING and STITCH both employ a statistical co-
thread. It is furthermore is inherently thread-safe, allowing for occurrence as well as natural language processing (NLP) for
perfect scalability in multi-threaded use and is both available as subsequent extraction of relations between the identified
a command-line tool and as a Python module that was molecular entities from Medline and the open-access subset of
generated in part by the Simplified Wrapper and Interface PubMed Central. These relations are integrated with evidence
Generator (SWIG). For real-time applications, we have from many other sources including experimental data and
developed a multi-threaded HTTP server that utilizes this manually curated pathway databases.
Python module to expose the tagger as a RESTful web service,
which includes support for the Open Annotation model [2]. B. Protein Localization and Expression
The COMPARTMENTS [13] and TISSUES [14] databases
The ability to perform real-time tagging enabled us to
take a very similar integrative approach to associate proteins
develop the Reflect [3] and EXTRACT [4] tools, which helps
with their subcellular localizations and tissue expression
curators identify and extract terms from any web page and was
patterns. To this end, we constructed dictionaries based on the
evaluated favourably in the interactive annotation track of
cellular component part of Gene Ontology [15] and the Brenda
BioCreative V [4].
Tissue Ontology [16], respectively. Both ontologies were well
populated with synonyms, which were automatically expanded
This work was in part funded by the Novo Nordisk Foundation
(NNF14CC0001) and the National Institutes of Health (U54 CA189205-01).
to construct plural and adjective forms. The resulting resources [2] S. Pyysalo, et al., “Sharing annotations better: RESTful Open
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(UMLS) Metathesaurus for all terms of the Medical Dictionary extraction: overview of the BioCreative V chemical-disease relation
for Regulatory Activities (MedDRA). In a separate study, we (CDR) task,” Vol. 2016, baw032, 2016.
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narrative text of electronic health records, which required the with user data,” Nucleic Acids Res., vol. 42, pp. D401–D407, 2014.
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D. Organisms and Environments/Habitats protein subcellular localization evidence,” Database, vol. 2014, bau012,
The applications described so far all fall within molecular 2014.
biomedicine; however, the tagger has proven equally useful [14] A. Santos, et al., “Comprehensive comparison of large-scale tissue
within biodiversity and ecology. Specifically, we have created expression datasets,” PeerJ, vol. 3, e1054, 2015.
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recall for species [1] and 87.8% precision and 77.0% recall for
environments [22]. We use these dictionaries with the tagger to [17] S. Pletscher-Frankild, et al, “DISEASES: text mining and data
integration of disease-gene associations,” Methods, vol. 74, pp. 83–89,
extract structured information on habitats of organisms based 2015.
on their textual descriptions in the Encyclopedia of Life [22]. [18] W. A. Kibbe, et al., “Disease Ontology 2015 update: an expanded and
Most recently we participated in the related 2016 BioNLP updated database of human diseases for linking biomedical knowledge
through disease data,” Nucleic Acids Res., vol. 43, pp. D1071–D1078,
shared task on bacterial biotopes, specifically NER of bacteria 2015.
and biotopes. To this end we implemented rules to refine the [19] S. ElShal, et al., “A comprehensive comparison of two MEDLINE
match boundaries and normalization of bacterial names and annotators for disease and gene linkage: sometimes less is more,”
compiled a biotope dictionary by extending the OntoBiotope Lecture Notes in Computer Science, vol. 9656, pp. 765–778, 2016.
habitat ontology with additional synonyms from other relevant [20] M. Kuhn, I. Letunic, L.J. Jensen, and P. Bork, “The SIDER database of
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[21] R. Eriksson, et al., “Dictionary construction and identification of
IV. CONCLUSIONS possible adverse drug events in Danish clinical narrative text,” J. Am.
Despite its simplicity, dictionary-based NER is a powerful Med. Inform. Assoc., vol. 20, pp. 947–953, 2013.
approach that in many cases can give comparable performance [22] E. Pafilis, et al., “ENVIRONMENTS and EOL: identification of
Environment Ontology terms in text and the annotation of the
to more advanced methods, if care is taken when constructing Encyclopedia of Life,” Bioinformatics, vol. 31, pp. 1872–1874, 2015.
the dictionaries. The dictionary-based approach is particularly
[23] S. Federhen, “Type material in the NCBI Taxonomy Database,” Nucleic
attractive in the biomedical domain due to the many ontologies Acids Res., vol. 43, pp. D1086–D1098, 2015.
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