1. Linguistic Analysis for Subject Identification
LASI
Linguistic Analysis for Subject Identification
Milestone Presentation
Presented by: CS410 Red Group
September 20, 2018

2. Outline Team Red Staff Chart Document Parsing Introduction Weighter
September 20, 2018
Outline
Team Red Staff Chart
Document Parsing
Introduction
Weighter
Problem Statement
GUI Flow
LASI in our Case Study
GUI Screenshots
Risk Matrix
Functional Components
Competition Matrix
Conclusion
Algorithms
Milestones

3. Team Red Staff Chart Scott Minter Dustin Patrick Richard Owens
September 20, 2018
Team Red Staff Chart
Scott Minter
Project Co Leader
Software Specialist
Brittany Johnson
Project Co Leader
Documentation Specialist
Dustin Patrick
Algorithm Specialist
Expert Liaison
Richard Owens
Documentation Specialist
Communication Specialist
Aluan Haddad
Algorithm Specialist
Software Specialist
Erik Rogers
Marketing Specialist
GUI Developer

4. September 20, 2018
What is LASI?

5. LASI: Linguistic Analysis for Subject Identification
September 20, 2018
LASI: Linguistic Analysis for Subject Identification
THEMES
LASI
LASI

6. LASI Identifies Themes (5 W’s & 1 H)
September 20, 2018
LASI Identifies Themes (5 W’s & 1 H)
Who
What
When
Where
Why
How

7. Why are themes important?
September 20, 2018
Why are themes important?
Comprehension
Summarization
Assists in communication between people

8. September 20, 2018
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.

9. September 20, 2018
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

10. What do we mean by “linguistic analysis”?
September 20, 2018
What do we mean by “linguistic analysis”?
The contextual study of written works and how the words combine to form an overall meaning.

11. September 20, 2018
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, objective assessments
Dr. Hester
Dr. Meyers

12. Before LASI yes Is the Customer satisfied?
September 20, 2018
Before LASI
Continue on to the rest of the A.I.D Process
Customer Contact
yes
Is the Customer satisfied?
Situational Awareness Meeting
Problem Statement Presentation no
Will NCSOSE be needed?
Document Gathering Process
Document Analysis
yes
Client Goes Elsewhere no

13. After LASI yes Is the Customer satisfied?
September 20, 2018
After LASI
Continue on to the rest of the A.I.D Process
Customer Contact
yes
Is the Customer satisfied?
Situational Awareness Meeting
Problem Statement Presentation no
Will NCSOSE be needed?
Document Gathering Process
Document Analysis
yes
Client Goes Elsewhere no

14. Major Functional Components
September 20, 2018
Major Functional Components
Hardware
Software
Algorithm:
Extrapolates the most likely congruence of themes and ideas across all documents in the input domain
High End Notebook PC
- Computation
Quad-Core CPU
- Primary Memory
8.0 GB DDR3 RAM
- Document Storage
Solid State Storage
~$1500 USD
User Interface:
- Multi-Level Views
- Weighted Phrase List
- Detailed Breakdown
- Step by Step Justification

15. Linguistic Analysis Algorithm
September 20, 2018
Linguistic Analysis Algorithm
Primary Analysis:
Word Count and Syntactic Assessment
Secondary Analysis:
Associative Identification
Tertiary Analysis:
Semantic Relationship Assessment
Traverse Document in Word-Wise Manner
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

16. September 20, 2018
LASI Milestones

17. September 20, 2018
Document Parsing

18. September 20, 2018
Weighter

19. September 20, 2018
GUI Flow

20. September 20, 2018
Splash Screen

21. September 20, 2018
New Project Screen

22. September 20, 2018
Results Page

23. Risk Matrix Customer Risks C1 -- Product Interest C2 -- Maintenance
September 20, 2018
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

24. Customer Risks C1. Product Interest C2. Maintenance C3. Trust
September 20, 2018
Customer Risks
C1. Product Interest
Probability 2 Impact 4
Mitigation: LASI offers unique functionality and user-friendliness.
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

25. Technical Risks T1. System Limitations T2. Scanned Text Recognition
September 20, 2018
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

26. Technical Risks T3. Jargon Recognition Probability 3 Impact 2
September 20, 2018
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

27. September 20, 2018
The Competition

28. Conclusion There is a need for LASI LASI is an algorithm heavy program
September 20, 2018
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

29. September 20, 2018
References
“Patrick Hester" Old Dominion University. N.p., n.d. Web. 24 Sept. 2012
<
"Tom Meyers." NCSOSE. N.p., n.d. Web. 22 Nov <
Stanislaw Osinski, Dawid Weiss. 13 August, Carrot 2. 9/25/2012 <
”WordStat” Provalis Research. Web. 24 Sept <
“ReadMe: Software for Automated Content Analysis” Web. 24 Sept <
readme.pdf>
"AlchemyAPI Overview." AlchemyAPI. N.p., n.d. Web. 19 Oct <
"AutoMap:." Project. N.p., n.d. Web. 19 Oct <
"CL Research Home Page." CL Research Home Page. N.p., n.d. Web. 19 Oct <