{"title":"在全文文章中标记基因和蛋白质名称","authors":"L. Tanabe, W. Wilbur","doi":"10.3115/1118149.1118151","DOIUrl":null,"url":null,"abstract":"Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE® abstracts. We apply a gene and protein name tagger trained on Medline abstracts (ABGene) to a randomly selected set of full text journal articles in the biomedical domain. We show the effect of adaptations made in response to the greater heterogeneity of full text.","PeriodicalId":339993,"journal":{"name":"ACL Workshop on Natural Language Processing in the Biomedical Domain","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"65","resultStr":"{\"title\":\"Tagging gene and protein names in full text articles\",\"authors\":\"L. Tanabe, W. Wilbur\",\"doi\":\"10.3115/1118149.1118151\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE® abstracts. We apply a gene and protein name tagger trained on Medline abstracts (ABGene) to a randomly selected set of full text journal articles in the biomedical domain. We show the effect of adaptations made in response to the greater heterogeneity of full text.\",\"PeriodicalId\":339993,\"journal\":{\"name\":\"ACL Workshop on Natural Language Processing in the Biomedical Domain\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"65\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACL Workshop on Natural Language Processing in the Biomedical Domain\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1118149.1118151\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACL Workshop on Natural Language Processing in the Biomedical Domain","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1118149.1118151","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tagging gene and protein names in full text articles
Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE® abstracts. We apply a gene and protein name tagger trained on Medline abstracts (ABGene) to a randomly selected set of full text journal articles in the biomedical domain. We show the effect of adaptations made in response to the greater heterogeneity of full text.
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