Overview of BioCreative II gene normalization

Morgan, Alexander A.; Lu, Zhiyong; Wang, Xinglong; Cohen, Aaron M.; Fluck, Juliane; Ruch, Patrick; Divoli, Anna; Fundel, Katrin; Leaman, Robert; Hakenberg, Jörg; Sun, Chengjie; Liu, Heng Hui; Torres, Rafael; Krauthammer, Michael; Lau, William; Liu, Hongfang; Hsu, Chun Nan; Schuemie, Martijn J.; Cohen, K. Bretonnel; Hirschman, Lynette

doi:10.1186/gb-2008-9-s2-s3

Cited by 262 publications

(233 citation statements)

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“…Approaches are typically evaluated on freely available resources like the BioCreative Gene Mention Task Corpus, to which we refer as BC2 (Smith et al, 2008), or the GENIA Corpus (Kim et al, 2003). When it comes to identifying particular proteins by linking the protein in question to some protein in an external database -a task we do not address in this paper -the BioCreative Gene Normalization Task Corpus is a common resource (Morgan et al, 2008).…”

Section: Related Workmentioning

confidence: 99%

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

Hartung¹,

Klinger²,

Zwick³

et al. 2014

Proceedings of BioNLP 2014

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Retrieving information about highly ambiguous gene/protein homonyms is a challenge, in particular where their non-protein meanings are more frequent than their protein meaning (e. g., SAH or HF). Due to their limited coverage in common benchmarking data sets, the performance of existing gene/protein recognition tools on these problematic cases is hard to assess.We uniformly sample a corpus of eight ambiguous gene/protein abbreviations from MEDLINE and provide manual annotations for each mention of these abbreviations. 1 Based on this resource, we show that available gene recognition tools such as conditional random fields (CRF) trained on BioCreative 2 NER data or GNAT tend to underperform on this phenomenon.We propose to extend existing gene recognition approaches by combining a CRF and a support vector machine. In a crossentity evaluation and without taking any entity-specific information into account, our model achieves a gain of 6 points F 1 -Measure over our best baseline which checks for the occurrence of a long form of the abbreviation and more than 9 points over all existing tools investigated.

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Section: Related Workmentioning

confidence: 99%

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

Hartung¹,

Klinger²,

Zwick³

et al. 2014

Proceedings of BioNLP 2014

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“…An example of the use of web services in evaluation from a related domain is the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) Challenge for annotation in the biomedical domain [15,16]. The approach for BioCreative II.5 5 was to have all participants install a web server to make their Annotation Servers available.…”

Section: Review Of Web-based and Component-level Evaluationmentioning

confidence: 99%

Automated Component–Level Evaluation: Present and Future

Hanbury

Müller

2010

Lecture Notes in Computer Science

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Abstract.Automated component-level evaluation of information retrieval (IR) is the main focus of this paper. We present a review of the current state of web-based and component-level evaluation. Based on these systems, propositions are made for a comprehensive framework for web service-based component-level IR system evaluation. The advantages of such an approach are considered, as well as the requirements for implementing it. Acceptance of such systems by researchers who develop components and systems is crucial for having an impact and requires that a clear benefit is demonstrated.

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“…The normalization task has been highlighted in the BioCreative challenges (Hirschman et al, 2005;Lu et al, 2011;Morgan et al, 2008), where a variety of methods have been explored for normalizing gene names, including string matching, pattern matching, and heuristic rules. Similar methods have been applied to disease names (Doğan & Lu, 2012b;Kang et al, 2012;Névéol et al, 2009) and species names (Gerner et al, 2010;Wei et al, 2012b), and the MetaMap program is used to locate and identify concepts from the UMLS MetaThesaurus Bodenreider, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Normalization is frequently decomposed into the sub-tasks of candidate generation and disambiguation Morgan et al, 2008). During candidate generation, the set of concept names is constrained to a set of possible matches using the text of the mention.…”

Section: Introductionmentioning

confidence: 99%

“…While machine learning methods for normalization have been explored (Tsuruoka et al, 2007;Wermter et al, 2009), these are far less common. This is partially due to the lack of appropriate training data, and also partially due to the need for a generalizable supporting framework.Normalization is frequently decomposed into the sub-tasks of candidate generation and disambiguation Morgan et al, 2008). During candidate generation, the set of concept names is constrained to a set of possible matches using the text of the mention.…”

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confidence: 99%

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Proceedings of BioNLP 2014

2014

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IntroductionThe first day of the BioNLP 2014 workshop continues following the course set by the first ACL workshop on Natural Language Processing in the Biomedical Domain that was held in 2002: BioNLP 2014 provides a venue for exploring challenges and techniques in processing biomedical language and brings together researchers from computational linguistics and biomedical informatics. The submissions to the first day of 2014 workshop organized by SIGBioMed were traditionally very strong and continued demonstrating the considerable breadth of research in biomedical language processing. The 2014 workshop has accepted 12 full and short papers for oral presentations and 7 posters. The first day of the workshop features a keynote that expands the scope of BioNLP beyond its already remarkable breadth The second day of the workshop features a paper submitted to the special track on NLP approaches for assessment of clinical conditions. Kathleen C. Fraser presents the featured talk on using statistical parsing to detect agrammatic aphasia. The track organizers, Thamar Solorio and Yang Liu, serve as discussants.The second day further features an exciting panel that brings together organizers of several shared tasks in biomedical information retrieval and natural language processing. The panel introduces the workshop participants to the long-standing and relatively new community-wide challenges in biomedical and clinical language processing. It also provides an opportunity to discuss the future of the shared tasks in this domain. AcknowledgmentsAs always, we are profoundly grateful to the authors who chose BioNLP as venue for presenting their innovative research. The authors' willingness to share their work through BioNLP consistently makes the workshop noteworthy among the increasing numbers of available venues. We are equally indebted to the program committee members (listed elsewhere in this volume) who produced at least three thorough reviews per paper on a tight review schedule and with an admirable level of insight.iii AbstractZoonotic viruses, viruses that are transmittable between animals and humans, represent emerging or re-emerging pathogens that pose significant public health threats throughout the world. It is therefore crucial to advance current surveillance mechanisms for these viruses through outlets such as phylogeography. Phylogeographic techniques may be applied to trace the origins and geographical distribution of these viruses using sequence and location data, which are often obtained from publicly available databases such as GenBank. Despite the abundance of zoonotic viral sequence data in GenBank records, phylogeographic analysis of these viruses is greatly limited by the lack of adequate geographic metadata. Although more detailed information may often be found in the related articles referenced in these records, manual extraction of this information presents a severe bottleneck. In this work, we propose an automated system for extracting this information using Natural Language Processing (NLP)...

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Overview of BioCreative II gene normalization

Cited by 262 publications

References 22 publications

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

Towards Gene Recognition from Rare and Ambiguous Abbreviations using a Filtering Approach

Automated Component–Level Evaluation: Present and Future

Proceedings of BioNLP 2014

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