LASI Linguistic Analysis for Subject Identification Milestone Presentation Presented by: CS410 Red Group 3/1/20 2 3/1/20 Outline Team Red Staff
Chart Introduction Problem Statement LASI in our Case Study Functional Components Algorithms Milestones Document Parsing
Weighter GUI Flow GUI Screenshots Risk Matrix Competition Matrix Conclusion 3 3/1/20 Team Red Staff Chart Brittany Scott Minter Project Co Leader Software Specialist
Johnson Project Co Leader Documentation Specialist Dustin Richard Patrick Owens Algorithm Specialist Expert Liaison Documentation Specialist
Communication Specialist Aluan Haddad Erik Rogers Algorithm Specialist Software Specialist Marketing Specialist GUI Developer 4 What is LASI? 3/1/20
5 3/1/20 LASI: Linguistic Analysis for Subject Identification LASI LASI THEMES 6 3/1/20
LASI Identifies Themes (5 Ws & 1 H) Who What When Where Why How 7 3/1/20
Why are themes important? Comprehension Summarization Assists in communication between people 8 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. 3/1/20 9
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 3/1/20 10 3/1/20 What do we mean by linguistic analysis? The contextual study of written works and how the words combine to form an overall
meaning. 11 3/1/20 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 Dr. Hester Dr. Meyers Formulate concise, objective assessments 12 3/1/20 Before LASI Continue on to the rest of the A.I.D Process Customer Contact
yes Is the Custome r satisfied ? Situational Awareness Meeting Will NCSOSE be needed? no
yes no Document Gathering Process Client Goes Elsewhere Problem Statement Presentation Document Analysis 13
3/1/20 After LASI Continue on to the rest of the A.I.D Process Customer Contact yes Is the Custome r satisfied ? Situational Awareness Meeting
Will NCSOSE be needed? no yes no Document Gathering Process Client Goes Elsewhere Problem
Statement Presentation Document Analysis 14 3/1/20 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
15 3/1/20 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-ObjectVerb Relationships Identify Potential Noun Phrases Output List of Weighted Themes Determine Frequency by Grammatical Role 16 LASI Milestones
23 3/1/20 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
3/1/20 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 3/1/20 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 3/1/20 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 The Competition 3/1/20 28 3/1/20 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
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