Linguistic text mining for problem reports
This paper describes a linguistic text mining tool for analyzing problem reports in aerospace engineering and safety organizations. The semantic trend analysis tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engine...
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| creator | Malin, J.T. Millward, C. Schwarz, H.A. Gomez, F. Throop, D.R. Thronesbery, C. |
| description | This paper describes a linguistic text mining tool for analyzing problem reports in aerospace engineering and safety organizations. The semantic trend analysis tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engineering discrepancy reports at NASA Johnson Space Center. The tool has been augmented with a statistical natural language parser that also resolves parsing gaps and identifies verb arguments and adjuncts. The tool uses an aerospace ontology augmented with features of taxonomies and thesauruses. The ontology defines hierarchies of problem types, equipment types and function types. STAT uses the output of the parser and the aerospace ontology to identify words and phrases in problem report descriptions that refer to types of hazards, equipment damage, performance deviations or functional impairments. Tool performance has been evaluated on 120 problem descriptions from problem reports, with encouraging results. |
| doi_str_mv | 10.1109/ICSMC.2009.5346056 |
| format | Conference Proceeding |
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The semantic trend analysis tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engineering discrepancy reports at NASA Johnson Space Center. The tool has been augmented with a statistical natural language parser that also resolves parsing gaps and identifies verb arguments and adjuncts. The tool uses an aerospace ontology augmented with features of taxonomies and thesauruses. The ontology defines hierarchies of problem types, equipment types and function types. STAT uses the output of the parser and the aerospace ontology to identify words and phrases in problem report descriptions that refer to types of hazards, equipment damage, performance deviations or functional impairments. 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Tool performance has been evaluated on 120 problem descriptions from problem reports, with encouraging results.</description><subject>Aerospace engineering</subject><subject>Aerospace safety</subject><subject>Data mining</subject><subject>Hazards</subject><subject>knowledge discovery</subject><subject>NASA</subject><subject>natural language understanding</subject><subject>Natural languages</subject><subject>Ontologies</subject><subject>ontology</subject><subject>Software safety</subject><subject>Text mining</subject><subject>USA Councils</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>9781424427932</isbn><isbn>1424427932</isbn><isbn>9781424427949</isbn><isbn>1424427940</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkE9LxDAUxOOfBbvrfgG99Cy0vpcmL8lRyqoLFQ8qeFuaNpXIdlvSCPrtLbgXTzPMD4ZhGLtCyBHB3G7Ll6cy5wAml4UgkHTC1kZpFFwIrowwpyzhUqkMScqzf6zg5yxBIJ4Zzt8XbDnXaANEGi7Ycpo-ATgI1Am7qfzh48tP0TdpdN8x7f1hTtJuCOkYBrt3fRrcOIQ4XbJFV-8ntz7qir3db17Lx6x6ftiWd1XmUcmYKSecRGUVWAGAslaSJBmDllpOuiPjNFnq5j1C2MK1om6aGelWEsymWLHrv17vnNuNwfd1-NkdPyh-AVUHSEc</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Malin, J.T.</creator><creator>Millward, C.</creator><creator>Schwarz, H.A.</creator><creator>Gomez, F.</creator><creator>Throop, D.R.</creator><creator>Thronesbery, C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200910</creationdate><title>Linguistic text mining for problem reports</title><author>Malin, J.T. ; Millward, C. ; Schwarz, H.A. ; Gomez, F. ; Throop, D.R. ; Thronesbery, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-7e4e517b70b40015a75656991b6d268f69e86b6f89044b3ed4acc6d28d560c6d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Aerospace engineering</topic><topic>Aerospace safety</topic><topic>Data mining</topic><topic>Hazards</topic><topic>knowledge discovery</topic><topic>NASA</topic><topic>natural language understanding</topic><topic>Natural languages</topic><topic>Ontologies</topic><topic>ontology</topic><topic>Software safety</topic><topic>Text mining</topic><topic>USA Councils</topic><toplevel>online_resources</toplevel><creatorcontrib>Malin, J.T.</creatorcontrib><creatorcontrib>Millward, C.</creatorcontrib><creatorcontrib>Schwarz, H.A.</creatorcontrib><creatorcontrib>Gomez, F.</creatorcontrib><creatorcontrib>Throop, D.R.</creatorcontrib><creatorcontrib>Thronesbery, C.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Malin, J.T.</au><au>Millward, C.</au><au>Schwarz, H.A.</au><au>Gomez, F.</au><au>Throop, D.R.</au><au>Thronesbery, C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Linguistic text mining for problem reports</atitle><btitle>2009 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2009-10</date><risdate>2009</risdate><spage>1578</spage><epage>1583</epage><pages>1578-1583</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>9781424427932</isbn><isbn>1424427932</isbn><eisbn>9781424427949</eisbn><eisbn>1424427940</eisbn><abstract>This paper describes a linguistic text mining tool for analyzing problem reports in aerospace engineering and safety organizations. The semantic trend analysis tool (STAT) helps analysts find and review recurrences, similarities and trends in problem reports. The tool is being used to analyze engineering discrepancy reports at NASA Johnson Space Center. The tool has been augmented with a statistical natural language parser that also resolves parsing gaps and identifies verb arguments and adjuncts. The tool uses an aerospace ontology augmented with features of taxonomies and thesauruses. The ontology defines hierarchies of problem types, equipment types and function types. STAT uses the output of the parser and the aerospace ontology to identify words and phrases in problem report descriptions that refer to types of hazards, equipment damage, performance deviations or functional impairments. Tool performance has been evaluated on 120 problem descriptions from problem reports, with encouraging results.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2009.5346056</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, p.1578-1583 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_5346056 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Aerospace engineering Aerospace safety Data mining Hazards knowledge discovery NASA natural language understanding Natural languages Ontologies ontology Software safety Text mining USA Councils |
| title | Linguistic text mining for problem reports |
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