May 2012 

NASA/TM-2012-217576 

NESC-RP-07-070 

 

Linguistic Preprocessing and Tagging for 

Problem Report Trend Analysis 

Robert J. Beil/NESC 

Langley Research Center, Hampton, Virginia 

Jane T. Malin 

Johnson Space Center, Houston, Texas 
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May 2012 

NASA/TM-2012-217576 

NESC-RP-07-070 

 

Linguistic Preprocessing and Tagging for 

Problem Report Trend Analysis 

Robert J. Beil/NESC 

Langley Research Center, Hampton, Virginia 

Jane T. Malin 

Johnson Space Center, Houston, Texas 
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Linguistic Preprocessing and Tagging for Problem Report Trend 

Analysis

March 29, 2012
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Report Approval and Revision History 

NOTE: This document was approved at the March 29, 2012, NRB. This document was 

submitted to the NESC Director on April 12, 2012, for configuration control.

Approved: Original Signature on File 4/18/12

NESC Director Date

Version Description of Revision Office of Primary 

Responsibility Effective Date

1.0 Initial Release Mr. Robert Beil, 

Systems Engineering 

Office, Kennedy Space 

Center

03/29/12
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Table of Contents

Technical Assessment Report

1.0 Notification and Authorization........................................................................................ 4

2.0 Signature Page................................................................................................................... 5

3.0 Team List........................................................................................................................... 6

3.1 Acknowledgements................................................................................................. 6

4.0 Executive Summary .......................................................................................................... 7

4.1 Problem Description ............................................................................................... 7

4.2 Background............................................................................................................. 7

4.3 Approach................................................................................................................. 7

4.4 Results..................................................................................................................... 8

5.0 Assessment Plan .............................................................................................................. 10

6.0 Problem Description and Proposed Solution ............................................................... 10

6.1 Problem Description ............................................................................................. 10

6.2 Proposed Solution ................................................................................................. 11

7.0 Prototype Tool Suite Evaluations.................................................................................. 13

7.1 Tagging Accuracy................................................................................................. 13

7.2 Improving Text Mining Accuracy ........................................................................ 13

7.3 Improving Analyst Productivity ........................................................................... 14

8.0 Findings, Observations, and NESC Recommendations .............................................. 15

8.1 Findings................................................................................................................. 15

8.2 Observations ......................................................................................................... 15

8.3 NESC Recommendations...................................................................................... 15

9.0 Alternate Viewpoints...................................................................................................... 16

10.0 Other Deliverables.......................................................................................................... 16

11.0 Lessons Learned.............................................................................................................. 16

12.0 Definition of Terms......................................................................................................... 16

13.0 Acronyms List ................................................................................................................. 17

14.0 References........................................................................................................................ 18

15.0 Appendices....................................................................................................................... 19

List of Figures

Figure 6.2-1. STAT Linguistic Analysis: Convert Text Fields into Metadata Tags .................. 12

List of Tables

Table 7.2-1. Recall and Precision Means and Ranges .............................................................. 14
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Technical Assessment Report

1.0 Notification and Authorization 

Mr. Robert Beil, Systems Engineer at Kennedy Space Center (KSC), requested the NASA 

Engineering and Safety Center (NESC) develop a prototype tool suite that combines 

complementary software technology used at Johnson Space Center (JSC) and KSC for problem 

report preprocessing and semantic tag extraction, to improve input to data mining and trend 

analysis. The technology developed at JSC includes text analysis software and the Aerospace 

Ontology (AO), which is a nomenclature designed to support semantic analysis of aerospace 

problem descriptions. The KSC technology is software for natural language understanding and 

question answering, developed at the University of Central Florida (UCF). This combined 

approach will be used to analyze a variety of NASA problem reports. 

An NESC out-of-board activity was approved by Ms. Dawn Schaible, Systems Engineering 

Office Manager, on December 7, 2007. Mr. Beil was selected to lead this assessment. Dr. Jane 

T. Malin (JSC), a member of the NESC Data Mining and Trending Working Group (DMTWG), 

is the project manager and co-principal investigator (co-PI). Dr. Fernando Gomez (UCF) is also 

a co-PI. Assessment plans were approved by the NESC Review Board (NRB) on 

December 13, 2007, and November 4, 2010. Status briefings were reviewed on February 26, 

2009; December 3, 2009; and November 4, 2010. 

The key stakeholders for this assessment are:

 Ms. Linda Bromley, Division Manager, Program Engineering Integration Office, JSC 

Engineering Directorate.

 Mr. Delmar Foster, Senior Quality Systems and Data Mining Analyst, KSC/United Space 

Alliance.

 Dr. Ali Shaykhian, Information Technology Relations Manager, Technical Integration 

Office, KSC.

 Ms. Irene Piatek, Engineering Discrepancy Report (DR) Metrics Team Lead, Systems 

Architecture and Integration Office, JSC Engineering Directorate.

 Dr. Jeffrey Dawson, Data Analysis and Trending, Knowledge Management Systems 

Office, NASA Safety Center (NSC).

 Dr. Allen Nikora, Manager, Software Element, Assurance Technology Program Office,

Jet Propulsion Laboratory (JPL) Office of Safety and Mission Success (OSMS).
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2.0 Signature Page

Submitted by: 

Team Signature Page on File

Mr. Robert J. Beil Date Dr. Jane T. Malin Date

Significant Contributors: 

Mr. Land D. Fleming Date Dr. Fernando Gomez Date

Dr. Carroll G. Thronesbery Date Dr. David R. Throop Date

Signatories declare the findings, observations, and NESC recommendations compiled in the 

report are factually based from data extracted from Program/Project documents, contractor 

reports, and open literature, and/or generated from independently conducted tests, analysis, and 

inspections.
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3.0 Team List

Name Discipline Organization

Core Team

Bob Beil NESC Lead KSC

Jane Malin Co-Principal Investigator JSC

Fernando Gomez Co-Principal Investigator UCF

David Throop Software The Boeing Company

Land Fleming Software-Methods MEI Technologies®

Carroll Thronesbery Software-ConOps S&K Aerospace

Michel Izygon Software-Ontology Tietronix

Hansen Schwartz Computer Science Graduate Student UCF

Christopher Millward Computer Science Graduate Student UCF

Pam Throckmorton MTSO Program Analyst LaRC

Administrative Support

Carolyn Snare Technical Writer LaRC/ATK

3.1 Acknowledgements 

The Assessment team thanks the additional contributors who used and evaluated versions of the 

prototype during development: Mr. Roger G. Schwarz of the JSC Engineering Systems 

Architecture and Integration Office; Dr. Sheena J. Miller of the JSC Engineering Division of 

Software, Robotics, and Simulation; and Dr. Daniel E. Erickson and Mr. Joel M. Wilf of the JPL 

OSMS.
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4.0 Executive Summary

4.1 Problem Description

Thousands of problem reports are generated for aerospace systems during hardware and software 

development, operations, and maintenance. Assessments of these problems are used to identify 

corrective actions to limit the potential for problem recurrence. Engineers and safety analysts 

need automated assistance to review large sets of problems during periodic assessments to find, 

verify, and assess groups of similar problem reports. Analysts have found that manual inspection 

with search (e.g., using cause codes or text keywords) has been impractical for large problem 

sets. Codes are too often misinterpreted during coding or search, or are out-of-date and 

inaccurate. Search and text mining can be hampered by the complexity of natural language. The 

results of statistical text mining often include meaningless groupings based on misleading 

regularities in the text. 

4.2 Background

Analysts have complained about the overwhelming difficulty of reviewing large volumes of 

problem reports to find unknown, but important, “needles in the haystack.” Text mining 

approaches for searching and clustering problem reports typically produced too many false 

alarms and too few hits. Precision and Recall are two commonly used measures of retrieval or 

classification accuracy. They are stated as percentages, where higher values indicate higher 

accuracy. Recall (i.e., percentage of all possible hits that are retrieved) has ranged from 

21 to 62 percent in studies of text mining accuracy from medical and biological abstracts and 

facts. Precision (i.e., percentage of retrieved that are correct) is another measure of accuracy. In 

the application domain of problem reports, the Precision measurement is less important than 

Recall, because false alarm cases can be removed. Previous development of the Semantic Text 

Analysis Tool (STAT) and the Aerospace Ontology (AO) nomenclature had shown that 

analyzing and tagging the text in problem descriptions resulted in improvements in analysis of 

Johnson Space Center (JSC) engineering discrepancy reports (DRs). The text interpretation was 

used to identify metadata tags that were added to the problem report data records. These 

enhanced data files were reused to structure analysis of the reports. New and significant problem 

types were discovered by analysts using the tool.

4.3 Approach 

The first solution proposed by this assessment was to improve STAT/AO retrieval accuracy and 

apply it to additional types of problem report data. Accuracy can be improved by using

advanced parsing software for syntactic analysis to better handle the complexity of natural 

language problem descriptions. Integration of syntactic analysis in the Minimal Clausal 
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Reconstruction (MCR) algorithm from the University of Central Florida (UCF) was expected to 

make tagging with concepts from the AO more precise. Accurate metadata tags from STAT/AO

were expected to improve the text mining results.

The second solution used in this assessment was an advanced multi-dimensional search tool. An 

open source tool, Flexible Information Access using Metadata in Novel Combinations 

(Flamenco), was enhanced to produce tables, graphs, and spreadsheets. In the enhanced 

Flamenco+ version, complex searches were expected to be simplified by combining codes with

the hierarchically structured tags from the AO, which indicated problem types and equipment 

types mentioned in the problem description.

Multiple NASA Centers were involved in the process of developing and enhancing the 

prototypes, so that the resulting tools would be applicable across the Agency. Development of 

extensive user guides was planned so that the tools could be customized to increase use.

4.4 Results

In summary, the prototype tool suite improved information retrieval and text mining. 

Evaluations of STAT/AO tagging before and after MCR integration showed tagging accuracy for 

problem reports was substantially improved. Recall was improved from 10 to 86 percent, and 

Precision was improved from 27 to 78 percent. These accuracy levels were significant 

improvements over search and text mining accuracy. Adding STAT/AO tags to problem report 

data records for text mining further improved text mining accuracy.

Analyst effort required for trend discovery and analysis and for generation of graphs and 

spreadsheets of problem report trends was reduced. JSC analysts who used the prototype tool 

suite during this assessment found that the support for quick retrieval and inspection of groups of 

similar problems was beneficial. The prototype tool was used to find intractable but important 

topics, which have been difficult to discover and to retrieve with search methods.

The Assessment team concluded that converting problem report text into structured data can

substantially reduce analysis effort while improving insight into problem-report trends. When 

problem-report text is converted to data by linguistic analysis and tagging, and the tagged data is 

used in text mining, retrieval accuracy can be significantly improved. When the tagged data is 

used in a modern multi-dimensional browser, analysts find it easier to search and filter problem 

reports and generate graphs and spreadsheets for further review. Not only is effort reduced, but 

there is improved insight into the problem-report data. 

Based on their findings and observations, the Assessment team recommends that the NESC Data 

Mining and Trending Working Group (DMTWG) identify and advocate other opportunities for 

tool delivery and problem reporting dataset demonstrations. The prototype needs to be tailored 

for new datasets. Limited tailoring has been performed for DRs and other data sets including 
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problem reports, safety analyses, and requirements. To help new users, documentation and tools 

have been developed for tailoring AO, STAT, and Flamenco+. These have been made available 

as a VirtualBox®Image or as a delivery of files for downloading and installation. Extensive 

examples, user guides, and other documentation have been included in the delivery. These files 

have been transferred to Kennedy Space Center (KSC) and NASA Safety Center (NSC) users.
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5.0 Assessment Plan

The goal of the proposed effort was to maximize NASA programs’ safety by improving analysis 

of problem recurrences and similarities, using linguistic preprocessing to extract key data from 

problem reports. Use of semantic annotations or tags was expected to improve trend analysis 

effectiveness by providing more understandable output in an analyst-friendly tool suite. The 

goal was to develop a prototype tool suite for NASA-wide use for text mining and trending. 

The objectives of the assessment included the development of a prototype tool suite that 

combined semantic text analysis and extraction technology being used at JSC (i.e., STAT and 

AO) and at KSC (i.e., natural language processing and adapted WordNet taxonomy from UCF) 

for problem report preprocessing and semantic tag extraction. Key extracted terms were used to 

improve input to data mining and trend analysis tools that process structured and unstructured 

data. This combined approach was used to analyze a variety of problem reports, including Space 

Shuttle Program problem reporting and corrective action data and JSC engineering DRs. 

In the first year of this 3-year project, the Assessment team focused on a tool suite for JSC and 

KSC stakeholders. During the next 2 years, the scope expanded to other NASA groups 

addressing trends and recurrences in problem reports. Initially, the tool suite included an 

analyst-friendly commercial text mining and search tool. During the second and third years, the 

enriched output was tested in other commercial text mining tools such as the Statistical Analysis 

System®Text Analytics software. During the follow-on work in the fourth year, the Assessment 

team collaborated with users at other NASA Centers who had new types of problem-report data, 

missions, and analysis goals. The extraction and trend analysis suite was applied to mishap 

reports from the NASA Incident Reporting Information System and to the JPL Incident Surprise 

Anomaly problem reports. For each case, the prototype tool suite was updated to assist with 

problem report analyses and assessment tasks. The goal of the follow-on work was to make at 

least two deliveries to user groups, with associated training and support. A major addition to the 

suite was the use of presentation software for web-based, faceted search, and browsing to explore 

enriched problem-reporting data, and to interactively and automatically produce tables and trend 

graphs. 

6.0 Problem Description and Proposed Solution

6.1 Problem Description

Engineers and safety analysts needed automated assistance to review large sets of problem 

reports during periodic assessments. Assessments typically target corrective actions that will

limit the potential for problem recurrence. To accomplish this goal, analysts need to explore the 

data to identify groups of similar problem reports and then analyze them. Analysts have found 

that manual inspection with search (e.g., using cause codes or text keywords) has been 
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impractical for large problem sets. It has been known that such codes and keywords can be 

misinterpreted during coding and search, or are out-of-date and inaccurate. Search and text 

mining are hampered by the complexity of natural language descriptions of problems. The 

results of statistical text mining often include meaningless groupings based on misleading 

regularities in the text. Thus, text mining has problems with too many false alarms and too few 

hits. References 1 and 6 describe typical text mining results, where Recall (percentage of all

possible hits that are retrieved) ranges from 21 to 62 percent.

6.2 Proposed Solution

The proposed solution is a prototype tool suite that uses advanced parsing and ontology to 

interpret the meaning of problem descriptions. A chart showing the tool suite is presented in 

Figure 6.2-1. After extracting and preprocessing the data, STAT calls a standard statistical 

parser and the MCR algorithm for syntactic analysis.

1

 STAT uses the terms and concepts in the 

AO for semantic interpretation and tagging of words and phrases in the clauses. 

The AO contains a hierarchical nomenclature for problem and equipment types for this purpose. 

The AO is a lexicalized ontology where each concept is extended with a list of words or phrases

that are possible text representations of the concept. A description of AO and its development is 

provided in reference 3. Reference 5 describes the tool suite and its performance in detail, at a 

stage when the MCR had not yet been integrated. Tools and methods for tailoring the AO have 

been developed, based on the Protégé open source ontology development tool [ref. 7]. With 

these tools, analysts can systematically tailor the AO with new words and phrases that describe 

their specialized terminology. Analysts can define new concepts and sub-concepts in the AO, for 

new types of objects and problems in their domains. 

STAT is designed to interpret complex natural language text and reduce false alarms and misses. 

Breaking out of the predefined code limitations lets problem descriptions speak for themselves. 

Limited codes or keywords can be replaced with metadata tags that convert text to data and

identify the potentially meaningful topics in the text. Unlike codes, these tags are not required to 

be mutually exclusive. Multiple tags can be associated with each problem report data record to 

enhance search and text mining.

The prototype tool suite includes an enhanced open-source faceted browser (Flamenco+) that

takes advantage of the tags. Flamenco+ was designed for flexible browsing and searching in 

large information spaces [ref. 2]. Browsing and filtering can be organized according to the 

hierarchical structures of tags (e.g., problem and equipment types). Searching can be simplified 

by using codes and tags. Flamenco+ was enhanced during this assessment to automatically and 

 

1 The MCR was developed and refined during this technical assessment [ref. 4]. The work developed by UCF on 

semantic interpretation in the MCR algorithm to handle verb ambiguities is provided in Appendix A. 
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interactively produce tables, spreadsheets, and trend graphs as the problem groups were filtered 

and refined.

Figure 6.2-1. STAT Linguistic Analysis: Convert Text Fields into Metadata Tags

The prototype tool needs to be tailored for new datasets. Limited tailoring has been performed 

for DRs and to process text from other data sets including problem reports, safety analyses, and 

requirements. To help new users, documentation and tools have been developed for tailoring 

AO, STAT, and Flamenco+. These tools have been made available as a VirtualBox®Image or as 

a delivery of files for downloading and installation. Extensive examples, user guides, and other 

documentation have been included in the delivery. These files have been transferred to KSC and 

NSC users. 
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7.0 Prototype Tool Suite Evaluations 

7.1 Tagging Accuracy

Evaluations of STAT before and after MCR integration showed that tagging accuracy for

problem reports was substantially improved [ref. 4]. The evaluation used 36 problem types and a 

sample of 200 DRs from a 2007–2008 dataset. In the sample, manual scoring indicated that 

101 of these reports matched at least one of the 36 problem types. Automated tagging showed 

that STAT provided Recall of 10 percent, and using MCR in STAT improved Recall to 

86 percent. MCR in STAT improved Precision to 78 percent over Precision with STAT alone

(i.e., 27 percent). 

The analyst’s goal was to increase Recall, to improve search by finding more true positives. 

Analysts find it easy to identify and eliminate a few false positives from an analysis set. The 

high levels of Recall for STAT with MCR compared favorably with those for the Textpresso

ontology-based text miner for biological literature, which achieved 62 percent Recall about 

worm genomes [ref. 6]. Tagging, using MCR in STAT, compared favorably with Recall of 

54 percent for search alone in a later evaluation using similar DR data (Table 7.2-1). The higher

level of Recall with STAT/MCR tags shows that linguistic analysis improves search performance 

by finding more of the true positives that the analysts need.

7.2 Improving Text Mining Accuracy

The QuantumText text miner was selected to evaluate how including STAT tags affected text 

miner performance. For this evaluation, STAT was included in the MCR algorithm, as shown in 

Figure 6.2-1. Two thousand DR records from fiscal year 2008 were used to investigate the effect 

of tagging on text mining. Ten test cases (i.e., Loosely Connected, Traceability Error, Unfit, Out 

of Limits, Bad Identifier, Debris, Electrically Disordered, Stained, Not Aligned, and Failed Start) 

were drawn from the set of 36 topics selected for the previous evaluation. Each case consisted of 

dataset true records that were identified by STAT analysis, QuantumText search, or text mining. 

There were 9 to 41 true records per case, for a total of 249 true records.

The Assessment team compared three ways of retrieving the DRs: string search alone, text 

mining alone, and text mining with STAT tags included in the data record and double weighted. 

QuantumText used five exemplars (i.e., true positives) selected from the search and five nonexemplars to generate a list of DRs ranked by similarity. In scoring the text mining performance, 

the 50 exemplars were excluded (i.e., the five exemplars for each of the 10 cases), so that there 

were 199 true records remaining.

With search items removed, the task for the text miner is more difficult and accuracy scores may 

drop with “sure things” removed. The number of records scored was 1.5 times the number of 

true records found by search. In this way, the scoring took into account the varying numbers of 
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true records (i.e., from 9 to 41). Each case was scored and the average accuracy scores across all 

cases were computed. Means and ranges for Recall and Precision are shown in Table 7.2-1.

Text mining without tags produced substantially fewer true positives and substantially more false 

positives than search; this reduced both measures of accuracy. Alternatively, using doubleweighted STAT tags compensated for these text mining problems and increased the number of 

total hits for analysts (i.e., 170 = 120 + 50 exemplars).

Table 7.2-1. Recall and Precision Means and Ranges

Analysts can use these methods to improve trend analysis, troubleshooting, and retrospective 

analysis. Although the text mining results were disappointing, STAT tags improved text mining 

performance. More details of this evaluation are available in reference 4.

7.3 Improving Analyst Productivity

The capability to filter and cluster problem reports increases because the STAT-generated tags 

increase the amount of metadata and also organize it hierarchically. More and better tags can 

improve exploration and trend analysis. The large number of possible tags increases the possible 

topics that can be considered. The tags do not need to be mutually exclusive. There are more 

tags than the limited number of outdated and confusing codes. Typically, these codes require so 

much interpretation (e.g., in pull-down menus without contextual help) during coding and access 

that the code definitions are lost to the users. Access to the explicit meanings in the STAT tags 

via AO concepts helps analysts understand what makes selected groups of problem reports 

similar. JSC DR analysts who used the prototype tool suite during this assessment found that the 

support for quick retrieval and inspection of groups of similar problems was beneficial. The 

prototype was used to find intractable but important topics, which had been difficult to discover 

and retrieve with search.

Tags can be combined with codes and with search in modern faceted multi-dimensional

browsers. An enhanced open-source faceted browser (i.e., Flamenco+) that takes advantage of 

the tags was used as part of the prototype tool suite. Using Flamenco+, browsing and filtering 

can be organized according to the AO hierarchical tag structure (e.g., problem and equipment 
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types). Searching can be simplified by using codes and tags. Flamenco+ was enhanced during 

this assessment to produce tables, spreadsheets, and trend graphs as a by-product of searching 

and browsing. Using Flamenco+, the analyst’s effort required for trend analysis and generation 

of spreadsheets of problem reports was reduced.

8.0 Findings, Observations, and NESC Recommendations

8.1 Findings

The following findings were identified:

F-1. For problem reports, linguistic analysis and tagging, using STAT with MCR and AO

improved Precision and Recall retrieval accuracy compared to search and text mining 

without STAT-generated tags.

F-2. Use of a modern, multi-dimensional faceted browser containing a combination of codes, 

keywords, and STAT-generated metadata to indicate problem types improves the 

capability to search, filter problem reports, and to generate graphs and spreadsheets for 

review.

F-3. The prototype AO, STAT, and Flamenco+ tool suite can be tailored to analyze the 

engineering DR database and data from other engineering projects.

8.2 Observations

The following observations were identified:

O-1. Converting problem report text into structured data substantially reduces analysis effort 

while improving insight into problem-report trends.

O-2. Documentation for data processing and end users was developed, so that AO, STAT, and 

Flamenco+ can be tailored for new datasets.

8.3 NESC Recommendations

The following NESC recommendations were identified and are directed toward the NESC 

DMTWG unless otherwise indicated:

R-1. Other opportunities for tool delivery, problem-reporting dataset demonstrations, and 

technology maturation should be identified and advocated. (F-1, F-2, O-1)

R-2. Users should tailor the AO, STAT, and Flamenco+ tool suite for specific datasets.

(F-3 and O-2)

 Tailoring support can be obtained from the Assessment team developers.
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9.0 Alternate Viewpoints

There were no alternate viewpoints identified during the course of this investigation by the 

Assessment team or the NRB quorum.

10.0 Other Deliverables

The STAT, AO, and Flamenco+ tools are available to NASA users from the JSC Spacecraft 

Software Engineering Branch as a VirtualBox®Image (.vdi file) or as a delivery of source and 

.gz files. Examples, users’ guides, and other documentation are included in the delivery. These 

files have been transferred to DMTWG users at KSC and Glenn Research Center for analysis of 

problem reports.

The STAT tutorial with instructions for processing new datasets is included in Appendix B. The 

STAT user guide for installation and running examples of data processing in STAT and 

Flamenco+ is included in Appendix C. The user guide and abbreviated user guide for 

maintaining and updating the AO are included in Appendix D. The Flamenco+ tutorial for 

analyzing problem reports is included in Appendix E. The User Guide for Trend Analysis with 

Flamenco+ is included in Appendix F. 

11.0 Lessons Learned

No applicable lessons learned were identified for entry into the NASA Lessons Learned 

Information System.

12.0 Definition of Terms 

Corrective Actions Changes to design processes, work instructions, workmanship practices, 

training, inspections, tests, procedures, specifications, drawings, tools, 

equipment, facilities, resources, or material that result in preventing, 

minimizing, or limiting the potential for recurrence of a problem. 

Finding A conclusion based on facts established by the investigating authority. 

Lessons Learned Knowledge or understanding gained by experience. The experience may 

be positive, as in a successful test or mission, or negative, as in a mishap 

or failure. A lesson must be significant in that it has real or assumed 

impact on operations; valid in that it is factually and technically correct; 

and applicable in that it identifies a specific design, process, or decision 

that reduces or limits the potential for failures and mishaps, or reinforces a 

positive result. 
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Observation A factor, event, or circumstance identified during the assessment that did 

not contribute to the problem, but if left uncorrected has the potential to 

cause a mishap, injury, or increase the severity should a mishap occur. 

Alternatively, an observation could be a positive acknowledgement of a 

Center/Program/Project/Organization’s operational structure, tools, and/or 

support provided.

Ontology Formal representation of the shared vocabulary and set of concepts that 

are used to name and describe entities in a domain, and the relationships 

between those concepts. The representation is usually includes a conceptsub-concept hierarchy. It can be used to reason about the domain entities.

Problem The subject of the independent technical assessment.

Proximate Cause The event(s) that occurred, including any condition(s) that existed 

immediately before the undesired outcome, directly resulted in its 

occurrence and, if eliminated or modified, would have prevented the 

undesired outcome.

Recommendation An action identified by the NESC to correct a root cause or deficiency 

identified during the investigation. The recommendations may be used by 

the responsible Center/Program/Project/Organization in the preparation of 

a corrective action plan.

Root Cause One of multiple factors (events, conditions, or organizational factors) that 

contributed to or created the proximate cause and subsequent undesired 

outcome and, if eliminated or modified, would have prevented the 

undesired outcome. Typically, multiple root causes contribute to an 

undesired outcome.

13.0 Acronyms List

AO Aerospace Ontology

ATK Alliant Techsystems, Inc.

ConOps Concept of Operations

DMTWG Data Mining and Trending Working Group

DR Discrepancy Reports

Flamenco+ Flexible Information Access Using Metadata in Novel Combinations

JPL Jet Propulsion Laboratory

JSC Johnson Space Center

KSC Kennedy Space Center

LaRC Langley Research Center

MCR Minimal Clausal Reconstruction
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MEI Technologies® Merging Excellence and Innovation Technologies®

MTSO Management and Technical Support Office

NESC NASA Engineering and Safety Center

NRB NESC Review Board

NSC NASA Safety Center

OSMS Office of Safety and Mission Success

PI Principal Investigator

S&K Aerospace Salish and Kootenai Aerospace

STAT Semantic Text Analysis Tool

UCF University of Central Florida

14.0 References

1. N. Daraselia, A. Yuryev, S. Egorov, S. Novichkova, A. Nikitin, and I. Mazo, “Extracting 

Human Protein Interactions from MEDLINE using a Full-sentence Parser,” Bioinformatics,

20 (5), 2004, pp. 604–611.

2. P. Hearst, “The Flamenco Search Interface Project,” http://flamenco.berkeley.edu/.

3. J. Malin and D. Throop, “Basic Concepts and Distinctions for an Aerospace Ontology of 

Functions, Entities and Problems,” 2007 IEEE Aerospace Conf. Proc., CD-ROM, IEEE

Aerospace, March 2007, pp. 1–18.

4. J. Malin, C. Millward, F. Gomez, and D. Throop, “Semantic Annotation of Aerospace 

Problem Reports to Support Text Mining,” IEEE Intelligent Systems, 25 (5), 

September/October 2010, pp. 20–26.

5. J. Malin, C. Millward, H. Schwarz, F. Gomez, D. Throop, and C. Thronesbery, “Linguistic 

Text Mining for Problem Reports,” 2009 IEEE Int’l Conf. Systems, Man and Cybernetics, 

2009, pp. 1578–1583. 

6. H-M. Müller, E. Kenny, and P. Sternberg, “Textpresso: An Ontology-Based Information 

Retrieval and Extraction System for Biological Literature,” PLoS Biol., November 2004, 

2(11), e309.

7. “Welcome to Protégé,” http://protege.stanford.edu/.
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15.0 Appendices 

Appendix A. From the MCR to Semantic Interpretation: A Progress Report

Appendix B. STAT Tutorial

Appendix C. STAT User Guide

Appendix D. User Guide of Maintaining and Updating the Aerospace Ontology

Appendix E. Tutorial: Analyzing Problem Reports with Flamenco+

Appendix F. The Flamenco+ Faceted Browser User Guide for Trend Analysis
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Appendix A. From the MCR to Semantic Interpretation: A Progress 

Report
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Appendix D. User Guides: Maintaining and Updating the Aerospace 

Ontology
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Appendix E. Tutorial: Analyzing Problem Reports with Flamenco+
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Appendix F. The Flamenco+ Faceted Browser User Guide for Trend 

Analysis
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REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Linguistic Preprocessing and Tagging for Problem Report Trend Analysis

5a. CONTRACT NUMBER

 6. AUTHOR(S)

Beil, Robert J.; Malin, Jane T.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

L-20148 NESC-RP-07-070

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 59 Mathematical and Computer Sciences (General)

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

Mr. Robert Beil, Systems Engineer at Kennedy Space Center (KSC), requested the NASA Engineering and Safety Center 

(NESC) develop a prototype tool suite that combines complementary software technology used at Johnson Space Center (JSC) 

and KSC for problem report preprocessing and semantic tag extraction, to improve input to data mining and trend analysis. 

This document contains the outcome of the assessment and the Findings, Observations and NESC Recommendations.

15. SUBJECT TERMS

Semantic Text Analysis Tool; Aerospace Ontology; Discrepancy reports; NASA Engineering and Safety Center; Minimal 

Clausal Reconstruction 

18. NUMBER

 OF 

 PAGES

251

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

a. REPORT

U

c. THIS PAGE

U

b. ABSTRACT

U

17. LIMITATION OF 

 ABSTRACT

UU

Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

December 2007 - March 2012

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

869021.02.06.01.06

11. SPONSOR/MONITOR'S REPORT

 NUMBER(S)

NASA/TM-2012-217576

16. SECURITY CLASSIFICATION OF:

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and 

Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person 

shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (DD-MM-YYYY)

01- 05 - 2012