一种短文本自然语言注释工具。

IF 1.3 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION Journal of Research of the National Institute of Standards and Technology Pub Date : 2019-11-01 eCollection Date: 2019-01-01 DOI:10.6028/jres.124.029
Thurston B Sexton, Michael P Brundage
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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.
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自引率
33.30%
发文量
10
审稿时长
>12 weeks
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.
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