Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

Rebholz‐Schuhmann, Dietrich; Jimeno-Yepes, Antonio; Li, Chen; Kafkas, Şenay; Lewin, Ian; Kang, Ning; Corbett, Peter; Milward, David; Buyko, Ekaterina; Beisswanger, Elena; Hornbostel, Kerstin; Kouznetsov, Alexandre; Witte, René; Laurila, Jonas Bergman; Baker, Christopher J. O.; Kuo, Chung‐Hao; Clematide, Simon; Rinaldi, Fabio; Farkas, Richárd; Móra, György; Hara, Kazuo; Furlong, Laura I.; Rautschka, Michael; Neves, Mariana; Pascual-Montano, Alberto; Wei, Qi; Collier, Nigel; Chowdhury, Faisal Mahbub; Lavelli, Alberto; Berlanga, Rafael; Morante, Roser; Asch, Vincent Van; Daelemans, Walter; Marina, José Luís; Mulligen, Erik M. van; Kors, Jan A.; Hahn, Udo

doi:10.1186/2041-1480-2-s5-s11

Cited by 48 publications

(45 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, dictionary-based approaches tend to miss undefined terms that are not mentioned in the dictionary [12]. The overall results of dictionary-based approaches rely heavily on a predefined dictionary.…”

Section: Introductionmentioning

confidence: 99%

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Zhu

Shen

2012

PLoS ONE

View full text Add to dashboard Cite

Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data.

show abstract

Section: Introductionmentioning

confidence: 99%

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Zhu

Shen

2012

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…For example, “4-hydroxybenzoate polyprenyltransferase” (UniProt:COQ2 YEAST) is an enzyme that requires the substrate “4-hydroxybenzoate” (ChEBI:17879). Many more such references can be expected across the Lexeome as has been demonstrated by the CALBC project [5].…”

Section: Introductionmentioning

confidence: 84%

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

et al. 2013

Self Cite

View full text Add to dashboard Cite

MotivationBiomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness).ResultThis study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions.ConclusionLexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources.

show abstract

“…Some of the best known are BioCreative (4), BioNLP (5) and CALBC (6). The 2012 BioCreative edition included, in particular, a task aiming at supporting the triage process for the Comparative Toxicogenomics Database.…”

Section: Introductionmentioning

confidence: 99%

Using the OntoGene pipeline for the triage task of BioCreative 2012

et al. 2013

Self Cite

View full text Add to dashboard Cite

In this article, we describe the architecture of the OntoGene Relation mining pipeline and its application in the triage task of BioCreative 2012. The aim of the task is to support the triage of abstracts relevant to the process of curation of the Comparative Toxicogenomics Database. We use a conventional information retrieval system (Lucene) to provide a baseline ranking, which we then combine with information provided by our relation mining system, in order to achieve an optimized ranking. Our approach additionally delivers domain entities mentioned in each input document as well as candidate relationships, both ranked according to a confidence score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation. Thanks, in particular, to the high-quality entity recognition, the OntoGene system achieved the best overall results in the task.

show abstract

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

Cited by 48 publications

References 9 publications

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

Using the OntoGene pipeline for the triage task of BioCreative 2012

Contact Info

Product

Resources

About