{"title":"一种短文本自然语言注释工具。","authors":"Thurston B Sexton, Michael P Brundage","doi":"10.6028/jres.124.029","DOIUrl":null,"url":null,"abstract":"Nestor is a software tool that annotates natural language CSV (comma-separated\n variable) files, with a UTF-8 (Unicode Transformation Format – 8-bit) encoding,\n using a process called tagging [1]. The objective of Nestor is to help analysts make\n their natural language data, which is often unstructured, filled with technical\n content, jargon, mispellings, and abbreviations, computable to improve analysis. An\n example of natural language data that could be input to Nestor and the subsequent\n output data and the corresponding output is shown in Table 1. The annotated datasets\n generated by Nestor (as either a CSV or .h5 file) can be used for different analysis\n techniques, such as failure prediction, problem hot spot identification, and\n maintenance technician expertise assessment, as shown in [2–10]. Currently, the\n majority of use cases involve maintenance in the engineering domain (manufacturing,\n mining, heating ventilation and air conditioning (HVAC)), however, any natural\n language CSV file with UTF-8 encoding can be input to Nestor.","PeriodicalId":54766,"journal":{"name":"Journal of Research of the National Institute of Standards and Technology","volume":"124 ","pages":"1-5"},"PeriodicalIF":1.3000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.6028/jres.124.029","citationCount":"16","resultStr":"{\"title\":\"Nestor: A Tool for Natural Language Annotation of Short Texts.\",\"authors\":\"Thurston B Sexton, Michael P Brundage\",\"doi\":\"10.6028/jres.124.029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nestor is a software tool that annotates natural language CSV (comma-separated\\n variable) files, with a UTF-8 (Unicode Transformation Format – 8-bit) encoding,\\n using a process called tagging [1]. The objective of Nestor is to help analysts make\\n their natural language data, which is often unstructured, filled with technical\\n content, jargon, mispellings, and abbreviations, computable to improve analysis. An\\n example of natural language data that could be input to Nestor and the subsequent\\n output data and the corresponding output is shown in Table 1. The annotated datasets\\n generated by Nestor (as either a CSV or .h5 file) can be used for different analysis\\n techniques, such as failure prediction, problem hot spot identification, and\\n maintenance technician expertise assessment, as shown in [2–10]. Currently, the\\n majority of use cases involve maintenance in the engineering domain (manufacturing,\\n mining, heating ventilation and air conditioning (HVAC)), however, any natural\\n language CSV file with UTF-8 encoding can be input to Nestor.\",\"PeriodicalId\":54766,\"journal\":{\"name\":\"Journal of Research of the National Institute of Standards and Technology\",\"volume\":\"124 \",\"pages\":\"1-5\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.6028/jres.124.029\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Research of the National Institute of Standards and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.6028/jres.124.029\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2019/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Research of the National Institute of Standards and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.6028/jres.124.029","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Nestor: A Tool for Natural Language Annotation of Short Texts.
Nestor is a software tool that annotates natural language CSV (comma-separated
variable) files, with a UTF-8 (Unicode Transformation Format – 8-bit) encoding,
using a process called tagging [1]. The objective of Nestor is to help analysts make
their natural language data, which is often unstructured, filled with technical
content, jargon, mispellings, and abbreviations, computable to improve analysis. An
example of natural language data that could be input to Nestor and the subsequent
output data and the corresponding output is shown in Table 1. The annotated datasets
generated by Nestor (as either a CSV or .h5 file) can be used for different analysis
techniques, such as failure prediction, problem hot spot identification, and
maintenance technician expertise assessment, as shown in [2–10]. Currently, the
majority of use cases involve maintenance in the engineering domain (manufacturing,
mining, heating ventilation and air conditioning (HVAC)), however, any natural
language CSV file with UTF-8 encoding can be input to Nestor.
期刊介绍:
The Journal of Research of the National Institute of Standards and Technology is the flagship publication of the National Institute of Standards and Technology. It has been published under various titles and forms since 1904, with its roots as Scientific Papers issued as the Bulletin of the Bureau of Standards.
In 1928, the Scientific Papers were combined with Technologic Papers, which reported results of investigations of material and methods of testing. This new publication was titled the Bureau of Standards Journal of Research.
The Journal of Research of NIST reports NIST research and development in metrology and related fields of physical science, engineering, applied mathematics, statistics, biotechnology, information technology.