LASI
Linguistic Analysis for Subject Identification
Milestone Presentation
Presented by: CS410 Red Group
July 26, 2016
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Outline
Team Red Staff Chart
Introduction
Problem Statement
LASI in our Case
Study
• Functional
Components
• Algorithms
• Milestones
•
•
•
•
•
•
•
•
•
•
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Document Parsing
Weighter
GUI Flow
GUI Screenshots
Risk Matrix
Competition Matrix
Conclusion
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Team Red Staff Chart
Scott Minter
Brittany Johnson
Project Co Leader
Software Specialist
Project Co Leader
Documentation Specialist
Dustin Patrick
Algorithm Specialist
Expert Liaison
Richard Owens
Documentation Specialist
Communication Specialist
Aluan Haddad
Erik Rogers
Algorithm Specialist
Software Specialist
Marketing Specialist
GUI Developer
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What is LASI?
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LASI: Linguistic Analysis for Subject Identification
LASI
LASI
THEMES
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LASI Identifies Themes (5 W’s & 1 H)
• Who
• What
• When
• Where
• Why
• How
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Why are themes important?
• Comprehension
• Summarization
• Assists in communication between people
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Societal Problem
It is difficult for people to identify a common
theme over a large set of documents in a
timely, consistent, and objective manner.
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Our Proposed Solution
• LASI is a linguistic analysis decision support
tool used to help determine a common
theme across multiple documents. It is our
goal with LASI to:
• accurately find themes
• be system efficient
• provide consistent results
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What do we mean by “linguistic analysis”?
The contextual study of written works and
how the words combine to form an overall
meaning.
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Dr. Patrick Hester & Dr. Tom Meyers: The AID Process
Assessment Improvement Design
• Dr. Hester & Dr. Meyers
are systems analysts and
researchers for NCSOSE
• Conduct extensive
research
• Quickly become familiar
with client systems
• Formulate concise,
Dr. Hester
Dr. Meyers
objective assessments
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Before LASI
Continue on to the rest of
the A.I.D Process
Customer
Contact
yes
Situational
Awareness
Meeting
Is the
Customer
satisfied?
Will
NCSOSE
be needed?
no
Document
Gathering
Process
no
yes
Client Goes
Elsewhere
Problem
Statement
Presentation
Document
Analysis
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After LASI
Continue on to the rest of
the A.I.D Process
Customer
Contact
yes
Situational
Awareness
Meeting
Is the
Customer
satisfied?
Will
NCSOSE
be needed?
no
Document
Gathering
Process
no
yes
Client Goes
Elsewhere
Problem
Statement
Presentation
Document
Analysis
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Major Functional Components
Hardware
Software
Algorithm:
High End Notebook PC
- Computation
Quad-Core CPU
- Primary Memory
8.0 GB DDR3 RAM
- Document Storage
Solid State Storage
~$1500 USD
Extrapolates the most
likely congruence of
themes and ideas across
all documents in the
input domain
User Interface:
- Multi-Level Views
- Weighted Phrase List
- Detailed Breakdown
- Step by Step
Justification
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Linguistic Analysis Algorithm
Primary Analysis:
Word Count and
Syntactic Assessment
Traverse Document in
Word-Wise Manner
Secondary Analysis:
Associative
Identification
Tertiary Analysis:
Semantic Relationship
Assessment
Bind Pronouns to Nouns,
Updating Frequency
Identify Potential
Synonyms
Identify Corresponding
Parts of Speech
Bind Adjectives to Nouns
Assess Potential
Subject-Object-Verb
Relationships
Determine Frequency by
Grammatical Role
Identify Potential Noun
Phrases
Output List of
Weighted Themes
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LASI Milestones
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Document Parsing
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Weighter
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GUI Flow
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Splash Screen
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New Project Screen
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Results Page
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Risk Matrix
Customer Risks
C1 -- Product Interest
C2 -- Maintenance
C3 -- Trust
Technical Risks
T1 -- System Limitations
T2 -- Scanned Text Recognition
T3 -- Jargon Recognition
T4 – Illegal Character Handling
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Customer Risks
C1. Product Interest
Probability 2
Impact 4
Mitigation: LASI offers unique functionality and userfriendliness.
C2. Maintenance
Probability 3
Impact 2
Mitigation: LASI will be a free, open source application
allowing the community to maintain and extend it over time.
C3. Trust
Probability 3
Impact 3
Mitigation: LASI will provide a step by step breakdown of
output analysis and algorithm reasoning
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Technical Risks
T1. System Limitations
Probability 4
Impact 2
Mitigation: LASI will be designed from the ground up
in native C++ for memory and CPU efficient code.
T2. Scanned Text Recognition
Probability 4
Impact 3
Mitigation: LASI will implement an optical character
recognition algorithm to handle scanned text
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Technical Risks
T3. Jargon Recognition
Probability 3 Impact 2
Mitigation: LASI will have domain specific dictionaries
and feature intuitive contextual inference.
T4. Illegal Character Handling
Probability 4 Impact 2
Mitigation: LASI will providers contextual inference,
synonym recognition and statistical methods
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The Competition
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Conclusion
• There is a need for LASI
• LASI is an algorithm heavy program
• Success is beneficial to anyone needing
to analyze large sets of documents in a
timely, consistent and objective manner
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References
“Patrick Hester" Old Dominion University. N.p., n.d. Web. 24 Sept. 2012
<http://www.odu.edu/directory/people/p/pthester>.
"Tom Meyers." NCSOSE. N.p., n.d. Web. 22 Nov. 2012.
<http://www.ncsose.org/index.php?option=com_jresearch>.
Stanislaw Osinski, Dawid Weiss. 13 August, 2012 . Carrot 2. 9/25/2012
<http://project.carrot2.org>.
”WordStat” Provalis Research. Web. 24 Sept. 2012.
<http://provalisresearch.com/products/content-analysis-software/>.
“ReadMe: Software for Automated Content Analysis” Web. 24 Sept. 2012.
<http://gking.harvard.edu/node/4520/rbuild_documentation/
readme.pdf>
"AlchemyAPI Overview." AlchemyAPI. N.p., n.d. Web. 19 Oct. 2012.
<http://www.alchemyapi.com/api/>.
"AutoMap:." Project. N.p., n.d. Web. 19 Oct. 2012.
<http://www.casos.cs.cmu.edu/projects/automap/>.
"CL Research Home Page." CL Research Home Page. N.p., n.d. Web. 19 Oct.
2012. <http://www.clres.com/>.