{"title":"使用隐马尔可夫模型和点互信息的无词典和无上下文的药品名称识别方法","authors":"Jacek Małyszko, A. Filipowska","doi":"10.1145/2390068.2390072","DOIUrl":null,"url":null,"abstract":"The paper concerns the issue of extraction of medicine names from free text documents written in Polish. Using lexicon-based approaches, it is impossible to identify unknown or misspelled medicine names. In this paper, we present the results of experimentation on two methods: Hidden Markov Model (HMM) and Pointwise Mutual Information (PMI)-based approach. The experiment was to identify the medicine names without the use of lexicon or contextual information. The experimentation results show, that HMM may be used as one of several steps in drug names' identification (with F-score slightly below 70% for the test set), while the PMI can help in increasing the precision of results achieved using HMM, but with significant loss in recall.","PeriodicalId":143937,"journal":{"name":"Data and Text Mining in Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Lexicon-free and context-free drug names identification methods using hidden markov models and pointwise mutual information\",\"authors\":\"Jacek Małyszko, A. Filipowska\",\"doi\":\"10.1145/2390068.2390072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper concerns the issue of extraction of medicine names from free text documents written in Polish. Using lexicon-based approaches, it is impossible to identify unknown or misspelled medicine names. In this paper, we present the results of experimentation on two methods: Hidden Markov Model (HMM) and Pointwise Mutual Information (PMI)-based approach. The experiment was to identify the medicine names without the use of lexicon or contextual information. The experimentation results show, that HMM may be used as one of several steps in drug names' identification (with F-score slightly below 70% for the test set), while the PMI can help in increasing the precision of results achieved using HMM, but with significant loss in recall.\",\"PeriodicalId\":143937,\"journal\":{\"name\":\"Data and Text Mining in Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data and Text Mining in Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2390068.2390072\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data and Text Mining in Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2390068.2390072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lexicon-free and context-free drug names identification methods using hidden markov models and pointwise mutual information
The paper concerns the issue of extraction of medicine names from free text documents written in Polish. Using lexicon-based approaches, it is impossible to identify unknown or misspelled medicine names. In this paper, we present the results of experimentation on two methods: Hidden Markov Model (HMM) and Pointwise Mutual Information (PMI)-based approach. The experiment was to identify the medicine names without the use of lexicon or contextual information. The experimentation results show, that HMM may be used as one of several steps in drug names' identification (with F-score slightly below 70% for the test set), while the PMI can help in increasing the precision of results achieved using HMM, but with significant loss in recall.