SPARQL queries have become the standard for querying linked open data knowledge bases, but SPARQL query construction can be challenging and time-consuming even for experts. SPARQL query generation from natural language questions is an attractive modality for interfacing with LOD. However, how to evaluate SPARQL query generation from natural language questions is a mostly open research question. This paper presents some issues that arise in SPARQL query generation from natural language, a test suite for evaluating performance with respect to these issues, and a case study in evaluating a system for SPARQL query generation from natural language questions.
{"title":"Evaluation of SPARQL query generation from natural language questions.","authors":"K Bretonnel Cohen, Jin-Dong Kim","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>SPARQL queries have become the standard for querying linked open data knowledge bases, but SPARQL query construction can be challenging and time-consuming even for experts. SPARQL query generation from natural language questions is an attractive modality for interfacing with LOD. However, how to evaluate SPARQL query generation from natural language questions is a mostly open research question. This paper presents some issues that arise in SPARQL query generation from natural language, a test suite for evaluating performance with respect to these issues, and a case study in evaluating a system for SPARQL query generation from natural language questions.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2013 ","pages":"3-7"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581285/pdf/nihms-982983.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38529044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electronic health records (EHRs) contain important clinical information about patients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.
{"title":"Topic Modeling Based Classification of Clinical Reports.","authors":"Efsun Sarioglu, Kabir Yadav, Hyeong-Ah Choi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Electronic health records (EHRs) contain important clinical information about patients. Some of these data are in the form of free text and require preprocessing to be able to used in automated systems. Efficient and effective use of this data could be vital to the speed and quality of health care. As a case study, we analyzed classification of CT imaging reports into binary categories. In addition to regular text classification, we utilized topic modeling of the entire dataset in various ways. Topic modeling of the corpora provides interpretable themes that exist in these reports. Representing reports according to their topic distributions is more compact than bag-of-words representation and can be processed faster than raw text in subsequent automated processes. A binary topic model was also built as an unsupervised classification approach with the assumption that each topic corresponds to a class. And, finally an aggregate topic classifier was built where reports are classified based on a single discriminative topic that is determined from the training dataset. Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2013 ","pages":"67-73"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544137/pdf/nihms-1932324.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41165188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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