1191 lines
88 KiB
Text
1191 lines
88 KiB
Text
<!DOCTYPE html>
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<html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# ">
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<head>
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<div class="entity-reference"> <a href="/news-events/news/2024/11/nist-hosts-annual-meeting-technical-language-processing-community-interest" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2024/11/29/NIST%20Hosts%20Annual%20Meeting%20for%20the%20Technical%20Language%20Processing%20Community%20of%20Interest.png?itok=ihFLLD-4" width="100" height="56" alt="NIST Hosts Annual Meeting for the Technical Language Processing Community of Interest" typeof="foaf:Image" />
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<h3 class="nist-teaser__title"><a href="/news-events/news/2024/11/nist-hosts-annual-meeting-technical-language-processing-community-interest"><span property="schema:name">NIST Hosts Annual Meeting for the Technical Language Processing Community of Interest</span>
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<div class="daterange"><time datetime="2024-11-01T12:00:00Z">November 1, 2024</time>
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|
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<div property="schema:text" class="text-with-summary"> On 24-25 September 2024, NIST hosted the annual meeting for the Technical Language Processing Community of Interest (TLP COI) . This year’s meeting centered on
|
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<h3 class="nist-teaser__title"><a href="/news-events/news/2024/07/nist-host-technical-language-processing-virtual-workshop-24-25-september"><span property="schema:name">NIST to Host Technical Language Processing Virtual Workshop on 24-25 September 2024</span>
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|
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<div class="nist-teaser__date">
|
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<div class="daterange"><time datetime="2024-07-01T12:00:00Z">July 1, 2024</time>
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<div class="nist-teaser__content">
|
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<div property="schema:text" class="text-with-summary"> NIST will hold a virtual workshop on Technical Language Processing on 24-25 September 2024. Online registration is $33.00 USD and is available via the workshop
|
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|
||
</div>
|
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|
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</div>
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<div>
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<article
|
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about="/news-events/news/2023/12/working-workspace-words-nist-helps-compose-technical-language-processing" typeof="schema:Article" class="nist-teaser"
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>
|
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<div class="nist-teaser__image">
|
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<div class="entity-reference"> <a href="/news-events/news/2023/12/working-workspace-words-nist-helps-compose-technical-language-processing" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2024/01/10/Working%20With%20Workspace%20Words%20NIST%20Helps%20Compose%20Technical%20Language%20Processing%20Tutorial%20at%20PHM%2023.png?itok=8Z_YVWI_" width="100" height="56" alt="Working With Workspace Words: NIST Helps Compose Technical Language Processing Tutorial at PHM 23" typeof="foaf:Image" />
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<h3 class="nist-teaser__title"><a href="/news-events/news/2023/12/working-workspace-words-nist-helps-compose-technical-language-processing"><span property="schema:name">Working With Workspace Words: NIST Helps Compose Technical Language Processing Tutorial at PHM 23</span>
|
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|
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|
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<div class="daterange"><time datetime="2023-12-01T12:00:00Z">December 1, 2023</time>
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|
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|
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<div class="nist-teaser__content">
|
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<div property="schema:text" class="text-with-summary"> NIST helped to foster awareness, debate, and education at the Technical Language Processing (TLP) Tutorial of the 15 th Annual Conference of the Prognostics and
|
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|
||
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|
||
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<h3 class="nist-teaser__title"><a href="/programs-projects/novel-root-and-rule-based-natural-language-processing-nlp-approach-information"><span>A novel root and rule-based natural language processing (NLP) approach to information indexing and searching</span>
|
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|
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<div class="text-with-summary"> Whether at the level of the individual, team, project or program, research and engineering work must keep abreast of huge amounts of published information that might contain discoveries or needed elements for discoveries crucial to its success. We, in collaboration with Software and Systems Division
|
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|
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|
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|
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||
|
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|
||
|
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<div>
|
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<article
|
||
about="/publications/human-loop-technical-document-annotation-developing-and-validating-system-provide" class="nist-teaser"
|
||
>
|
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<div class="nist-teaser__content-wrapper">
|
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|
||
<h3 class="nist-teaser__title"><a href="/publications/human-loop-technical-document-annotation-developing-and-validating-system-provide"><span>Human-in-the-loop Technical Document Annotation: Developing and Validating a System to Provide Machine-Assistance for Domain-Specific Text Analysis</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__date">
|
||
<div class="daterange"><time datetime="2024-05-14T12:00:00Z">May 14, 2024</time>
|
||
</div>
|
||
|
||
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|
||
<div class="nist-teaser__type">
|
||
<div class="nist-field nist-field--label-inline text-long">
|
||
<div class="nist-field__label">Author(s)</div>
|
||
<div class="nist-field__item"><span class="nist-author">Juan Fung</span>, <span class="nist-author">Zongxia Li</span>, <span class="nist-author">Daniel Stephens</span>, Andrew Mao, Pranav Goel, Emily Walpole, <span class="nist-author">Alden A. Dima</span>, Jordan Boyd-Graber</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> In this report, we address the following question: to what extent can machine learning assist a human with traditional text analysis, such as content analysis
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
|
||
</div>
|
||
<div>
|
||
<article
|
||
about="/publications/overview-trec-2023" class="nist-teaser"
|
||
>
|
||
<div class="nist-teaser__content-wrapper">
|
||
<header>
|
||
<h3 class="nist-teaser__title"><a href="/publications/overview-trec-2023"><span>Overview of TREC 2023</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__date">
|
||
<div class="daterange"><time datetime="2024-05-01T12:00:00Z">May 1, 2024</time>
|
||
</div>
|
||
|
||
</div>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-field nist-field--label-inline text-long">
|
||
<div class="nist-field__label">Author(s)</div>
|
||
<div class="nist-field__item"><span class="nist-author">Ian Soboroff</span></div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> TREC 2023 is the thirty-second edition of the Text REtrieval Conference (TREC). The main goal of TREC is to create the evaluation infrastructure required for
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
</article>
|
||
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<article
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about="/publications/improving-tenor-labeling-re-evaluating-topic-models-content-analysis" class="nist-teaser"
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>
|
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<div class="nist-teaser__content-wrapper">
|
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<header>
|
||
<h3 class="nist-teaser__title"><a href="/publications/improving-tenor-labeling-re-evaluating-topic-models-content-analysis"><span>Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis</span>
|
||
</a></h3>
|
||
<div class="nist-teaser__date">
|
||
<div class="daterange"><time datetime="2024-03-23T12:00:00Z">March 23, 2024</time>
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</div>
|
||
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</div>
|
||
<div class="nist-teaser__type">
|
||
<div class="nist-field nist-field--label-inline text-long">
|
||
<div class="nist-field__label">Author(s)</div>
|
||
<div class="nist-field__item">Zongxia Li, Andrew Mao, Jordan Boyd-Graber, Daniel Stephens, <span class="nist-author">Emily Walpole</span>, <span class="nist-author">Alden A. Dima</span>, <span class="nist-author">Juan Fung</span></div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Topic models are a popular tool for understanding text collections, but their evaluation has been a point of contention. Automated evaluation metrics such as
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
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about="/publications/kpi-extraction-maintenance-work-orders-comparison-expert-labeling-text-classification" class="nist-teaser"
|
||
>
|
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<div class="nist-teaser__content-wrapper">
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<header>
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<h3 class="nist-teaser__title"><a href="/publications/kpi-extraction-maintenance-work-orders-comparison-expert-labeling-text-classification"><span>KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines</span>
|
||
</a></h3>
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<div class="nist-teaser__date">
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<div class="daterange"><time datetime="2023-12-06T12:00:00Z">December 6, 2023</time>
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</div>
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</div>
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||
<div class="nist-teaser__type">
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<div class="nist-field nist-field--label-inline text-long">
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||
<div class="nist-field__label">Author(s)</div>
|
||
<div class="nist-field__item">Marc-Alexander Lutz, Bastian Schäfermeier, <span class="nist-author">Rachael Sexton</span>, <span class="nist-author">Michael Sharp</span>, <span class="nist-author">Alden A. Dima</span>, Stefan Faulstich, Jagan Mohini Aluri</div>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</header>
|
||
<div class="nist-teaser__content">
|
||
<div class="text-with-summary"> Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
||
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about="/services-resources/software/nestor" class="nist-teaser"
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<div class="entity-reference"> <a href="/services-resources/software/nestor" hreflang="en"><img loading="lazy" src="/sites/default/files/styles/thumbnail/public/images/2019/08/05/nestor_screenshot.png?itok=YY1CitQB" width="100" height="79" alt="A screenshot of the Nestor GUI " typeof="foaf:Image" />
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<div class="daterange"><time datetime="2019-05-23T12:00:00Z">May 23, 2019</time>
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|
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<div class="nist-teaser__content">
|
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<div class="text-with-summary"> The Nestor Graphical User Interface (GUI) is a free toolkit that helps maintainers annotate their Maintenance Work Order (MWO) data through a process called
|
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