{"title":"建立COVID-19医学研究文献的策划主题模型","authors":"P. Resnik, K. Goodman, Mike Moran","doi":"10.18653/v1/2020.nlpcovid19-2.30","DOIUrl":null,"url":null,"abstract":"Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.","PeriodicalId":131251,"journal":{"name":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Developing a Curated Topic Model for COVID-19 Medical Research Literature\",\"authors\":\"P. Resnik, K. Goodman, Mike Moran\",\"doi\":\"10.18653/v1/2020.nlpcovid19-2.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.\",\"PeriodicalId\":131251,\"journal\":{\"name\":\"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18653/v1/2020.nlpcovid19-2.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2020.nlpcovid19-2.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Developing a Curated Topic Model for COVID-19 Medical Research Literature
Topic models can facilitate search, navigation, and knowledge discovery in large document collections. However, automatic generation of topic models can produce results that fail to meet the needs of users. We advocate for a set of user-focused desiderata in topic modeling for the COVID-19 literature, and describe an effort in progress to develop a curated topic model for COVID-19 articles informed by subject matter expertise and the way medical researchers engage with medical literature.
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