GENIA corpus—a semantically annotated corpus for bio-textmining

Kim, Jin-Dong; Ohta, Tomoko; Tateisi, Yuka; Tsujii, Jun’ichi

doi:10.1093/bioinformatics/btg1023

Cited by 873 publications

(609 citation statements)

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“…The in-domain monolingual corpora include (cf. Table 2): the Cochrane database of reviews of primary research in human health care and health policy [63], DrugBank -a bioinformatics and cheminformatics resource describing drugs [64], Gene Regulation Event Corpus (GREC) -a semantically annotated English corpus of abstracts of biomedical texts [65], the GENIA corpus of biomedical literature compiled and annotated within the GENIA project [66], the Foundational Model of Anatomy Ontology (FMA) -a knowledge source for biomedical informatics concerned with symbolic representation of the phenotypic structure of the human body [67], English texts extracted from the UMLS Metathesaurus [40], the Patient Information Leaflet Corpus (PIL) -a collection of documents giving instructions to patients about their medication [69], and finally, a large set of texts extracted from HONcode-certified sites (HON) that have been identified by language-detection libraries [70,71] to be English-language [72].…”

Section: In-domain Monolingual Corporamentioning

confidence: 99%

Adaptation of machine translation for multilingual information retrieval in the medical domain

Pecina¹,

Dušek²,

Goeuriot³

et al. 2014

Artificial Intelligence in Medicine

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Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR.Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets.Results. The search query translation results achieved in our experiments are outstanding -our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results.Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance -better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.

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Section: In-domain Monolingual Corporamentioning

confidence: 99%

Adaptation of machine translation for multilingual information retrieval in the medical domain

Pecina¹,

Dušek²,

Goeuriot³

et al. 2014

Artificial Intelligence in Medicine

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“…[11] is consistent with [14] only if either (a) every instance of non-Saccharomyces Fungus polarized growth is co-localized with an instance of Saccharomyces polarized group or (b) there is Fungus polarized growth only in Saccharomyces. (a) we take to be biologically false; but (b) implies that 'site of polarized growth (sensu Saccharomyces)' and 'site of polarized growth (sensu Fungi)' in fact refer, confusingly, to the same class, and thus that the latter should be removed from GO's cellular component ontology.…”

Section: Problems With 'Sensu'mentioning

confidence: 95%

“…from which we can infer that: [13] bud tip is a site of polarized growth (sensu Fungi) and from there to: [14] every instance of bud tip has an instance of Fungus polarized growth located therein.…”

Section: Problems With 'Sensu'mentioning

confidence: 99%

“…Here we shall focus on isolating such shortfalls and on suggestions as how they might be avoided in the future. Such avoidance is, we shall argue, a prerequisite for the use of GO in conjunction with tools for advanced data integration of a sort which meet the standards of contemporary biomedical informatics, including tools for alignment of ontologies [12,13], ontology based text-mining [14][15][16] and 'intelligent' multi-database querying.…”

Section: Introductionmentioning

confidence: 99%

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On the Application of Formal Principles to Life Science Data: a Case Study in the Gene Ontology

Smith

Köhler

Kumar³

2004

Lecture Notes in Computer Science

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Abstract. Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration.

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“…Finally, the event extractor finds the binary relation using the syntactic information of a given sentence, co-occurrence statistics between two named entities, and pattern information of an event verb. General medical term was trained with UMLS meta-thesaurus [12] and the biological entity and its interaction was trained with GENIA [13] corpus. The underlying NLP approaches for named entity recognition are based on the system of Hwang et al [14] and Lee et al [15] with collaborations.…”

Section: Interaction Extractionmentioning

confidence: 99%

PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining

Eom

Zhang

2004

Artificial Intelligence: Methodology, Systems, and Applications

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Abstract. PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature is introduced. PubMiner utilize natural language processing and machine learning based data mining techniques for mining useful biological information such as protein-protein interaction from the massive literature data. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language analysis. The extracted interactions are further analyzed with a set of features of each entity which were constructed from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The evaluation of system performance proceeded with the protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD.

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GENIA corpus—a semantically annotated corpus for bio-textmining

Abstract: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.

Cited by 873 publications

References 1 publication

Adaptation of machine translation for multilingual information retrieval in the medical domain

Adaptation of machine translation for multilingual information retrieval in the medical domain

On the Application of Formal Principles to Life Science Data: a Case Study in the Gene Ontology

PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining

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