ProMiner: rule-based protein and gene entity recognition

Hanisch, Daniel; Fundel, Katrin; Mevissen, Heinz-Theodor; Zimmer, Ralf; Fluck, Juliane

doi:10.1186/1471-2105-6-s1-s14

Cited by 273 publications

(176 citation statements)

References 17 publications

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“…A strong focus in biomedical information extraction has long been on named entity recognition, for which machine-learning solutions such as conditional random fields (Lafferty et al, 2001) or dictionary-based systems (Schuemie et al, 2007;Hanisch et al, 2005;Hakenberg et al, 2011) are available which tackle the respective problem with decent performance and for specific entity classes such as organisms (Pafilis et al, 2013) or symptoms (Savova et al, 2010;Jimeno et al, 2008). A detailed overview on named entity recognition, covering other domains as well, can be found in Nadeau and Sekine (2007).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Third Workshop on Semantic Web and Information Extraction

2014

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IntroductionThere is a vast wealth of information available in textual format that the Semantic Web cannot yet tap into: 80% of data on the Web and on internal corporate intranets is unstructured, hence analysing and structuring the data -social analytics and next generation analytics -is a large and growing endeavour. Here, the Information Extraction community could help as they specialise in mining the nuggets of information from text. Information Extraction techniques could be enhanced by annotated data or domain-specific resources. The Semantic Web community has taken great strides in making these resources available through the Linked Open Data cloud, which are now ready for uptake by the Information Extraction community. Following the previous two SWAIE workshops at EKAW 2012 and RANLP 2013 respectively, we have focused our attention on fostering awareness of how Semantic Web technologies can benefit the traditional IE and NLP communities.The workshop invited contributions around three particular topics: 1) Semantic Web-driven Information Extraction, 2) Information Extraction for the Semantic Web, and 3) applications and architectures on the intersection of Semantic Web and Information Extraction SWAIE 2014 had a number of high-quality submissions. From these, 6 high quality papers were selected. AbstractOntologies have proven to be useful to enhance NLP-based applications such as information extraction. In the biomedical domain rich ontologies are available and used for semantic annotation of texts. However, most of them have either no or only few non-English concept labels and cannot be used to annotate non-English texts. Since translations need expert review, a full translation of large ontologies is often not feasible. For semantic annotation purpose, we propose to use the corpus to be annotated to identify high occurrence terms and their translations to extend respective ontology concepts. Using our approach, the translation of a subset of ontology concepts is sufficient to significantly enhance annotation coverage. For evaluation, we automatically translated RadLex ontology concepts from English into German. We show that by translating a rather small set of concepts (in our case 433), which were identified by corpus analysis, we are able to enhance the amount of annotated words from 27.36 % to 42.65 %.

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Section: Related Workmentioning

confidence: 99%

Proceedings of the Third Workshop on Semantic Web and Information Extraction

2014

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“…MEDLINE (Medical Literature Analysis and Retrieval System Online) is a bibliographic database maintained by the National Center for Biotechnology Information and covers a large number of scientific publications from medicine, psychology, and the health system. For the clustering use case, we study MEDLINE abstracts and associated metadata that are processed by ProMiner, a named entity recognition system ( [19]), and indexed by the semantic information retrieval platform SCAIView ( [20]). SCAIView also offers an API that allows programmatic access to the data.…”

Section: Document Clustering On Medlinementioning

confidence: 99%

Document Clustering using a Graph Covering with Pseudostable Sets

Dörpinghaus

Schaaf

Fluck

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. In this paper we present a new graph theoretical approach to document clustering and its application on a real-world data set. We will show that the wellknown graph partition to stable sets or cliques can be generalized to pseudostable sets or pseudocliques. This allows to make a soft clustering as well as a hard clustering. We will present an integer linear programming and a greedy approach for this NP-complete problem and discuss some results on random instances and some real world data for different similarity measures.

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“…It extracted material names in the sentence with 94.70% precision and 98.84% recall. D Hanisch et al [20] constructed the ProMiner system by a rule-based approach and a pre-processed synonym dictionary, to identify potential name occurrences in the bio-medical text and associate protein and gene database identifiers with the detected matches. In blind predictions, the system achieved an F-measure of approximately 0.8 for the organisms mouse and fly and about 0.9 for the organism yeast.…”

Section: The Rule-based Ner Approachmentioning

confidence: 99%

A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining

Sun¹,

Liu²,

Cai³

et al. 2017

Preprint

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At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

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ProMiner: rule-based protein and gene entity recognition

Cited by 273 publications

References 17 publications

Proceedings of the Third Workshop on Semantic Web and Information Extraction

Proceedings of the Third Workshop on Semantic Web and Information Extraction

Document Clustering using a Graph Covering with Pseudostable Sets

A Survey of Data Processing of EMR (Electronic Medical Record) Based on Data Mining

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