Deep Linguistic Analysis Business Plans
Order ID 53563633773 Type Essay Writer Level Masters Style APA Sources/References 4 Perfect Number of Pages to Order 5-10 Pages Description/Paper Instructions
Deep Linguistic Analysis Business Plans
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Compendia DATA PLATFORM Discussion with AFRL/STO
10 Nov 2021
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Agenda 1 Introductions
2 Compendia product Overview & Background
3 Demonstration
4 Questions & Answers
5 Next Steps
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Who is Titan Technologies?
2000 Averstar, Pacer
Infotec Joins Titan Corporation
2001 BTG Joins Titan
2006 Titan Joins
L-3 Communications
2014 Data Tactics joins L-3 NSS
2016 L-3 NSS Joins
CACI
2017 CACI Divests
Titan Technologies
A legacy OF SUCCESS
3
Our leadership team has been implementing complex technology solutions for Defense and secure Federal customers for more than 30 years.
TOP SECRET FACILITY CLEARANCE
200009001:2015
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Technology Partners
Expert System USA’s Natural Language Processor software (Expert NLP) is a sophisticated Commercial-off-the-Shelf (COTS) Natural
Language Processing/Understanding (NLP/NLU) software suite which enables an organization to process textual content and autogenerate sophisticated metadata to organize, discover, and explore information and power analytic tools. With Expert NLP, the data management paradigm shifts from time-intensive manual tagging and one-dimensional search to comprehensive auto-generated metadata and rapid and precise discovery of information. Expert NLP is operational in the Defense, Intelligence and Law Enforcement communities.
4 NNData’s Mission is to deliver a data management platform that supports “Machine Learning for the Masses”. Their NNCompass product is a complete AI-enabled data management platform that lets you get a handle on the most difficult ETL, streaming, data flow, preparation & transformation tasks faced in the enterprise today. NNCompass is operational in the Defense, Intelligence and Enterprise SW communities.
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Titan Team
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- David Ramirez – Chief Operating Officer
- Michael Edwins – Chief Growth Officer
- Belay Tekalign – VP, Mid-Atlantic Region, LANL/NSRC Program Manager
- Rob Buntz – Director, Digital Business Transformation
- Brian Welde – Compendia Product Manager
- Kevin Collins – Compendia Architect
- Kenny Kawahara – Compendia Engineer
- Gerald Gay – Chief Technology Officer
- Kevin Cousin, PhD – Chief Data Scientist
- Randy Garrett, PhD – Chief Data Officer
- Steve Aberle – Compendia Engineer
- Giuseppe Strafforello – Chief Technology Officer
- Emily Pace – Principal Linguist
- Erica Giorda – Product Manager
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AFRL Team
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- Dr. David Van Veldhuizen
- Kenneth Norman
- Stacie Tawney
- Urie Reed
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- Compendia product Overview
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Origin Story – NSRC Collaboration
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What’s in a name?
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A compendium is plural for compendium.
A compendium is a collection of concise but detailed information about a particular subject, especially in a book or other publication.
or
A collection of things, especially one systematically gathered.
COMPENDIA Oxford Languages
Titan’s Compendia is a flexible and modular orchestrator for assembling knowledge.
GET DO
Information Process Knowledge
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- Data professionals use Compendia’s AI & ML-enabled ETL environment to ingest and transform unstructured, semi- structured, and structured content into useful information.
- Data curators use Compendia to rapidly generate high quality metadata and prepare information for use in applications like search, visual analytics, business intelligence, and records management.
- Compendia’s NLU-enabled precision search and recall capability yields more useful answers to complex research questions helping analysts and specialists to get to insight faster.
DOE National Lab ROI: 2,000 researchers = $34M wasted annually
A data enrichment & search solution that enables organizations to more effectively organize, discover, and derive insights from their information resources
Compendia for research
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Key technology – Optical character recognition
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©2021, Titan Technologies, LLC. Proprietary & Confidential©2021, Titan Technologies, LLC. Proprietary & Confidential Key technology – natural language Processing / Understanding
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Deep Linguistic Analysis
Supports sophisticated natural language understanding by parsing each document sentence into tokens, lemmas and parts-of-speech
Knowledge Graph Empowered
Resolves ambiguous terms to their precise meaning, and determines overall document sentiment
Entity Extraction & Linking
Recognizes People, Companies, Locations and other Entities
“Fire” as the event of something burning vs. “Fire” as a dismissal vs. “Fire” as discharging ammunition vs. “Fire” as stir up or inflame
Relationships
Disambiguation
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Text Metadata-Enriched Text
Knowledge Graph
NLP/NLU Engine
Language Understanding
Linguistic Projects
Text Categorization Entity Extraction
Applications & Products • Search • Content Management • Text Analytics • Natural Language API • Compendia
Studio
Development Environment
Natural Language Processing / Natural Language Understanding (NLP / NLU)
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Natural Language Processing for Document Understanding
Deep Linguistic Analysis
Text Subdivision
Part of Speech Tagging
Morphological Analysis
Lemmatization
Syntactic Analysis
Semantic Analysis
Document Understanding
Topic Classification
Entity Extraction
Relationships
Sentiment
Standard Extractions
Custom Extractions
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Knowledge Graph
- Natural Language Processing pipeline semantic network • English Knowledge Graph contains ≈450k concepts and millions of links • Can incorporate unique customer knowledge and focus areas
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Comprehensive Off-the-Shelf Linguistic Projects
*Additional specialized taxonomies and custom entity extractions are available (including military and intelligence entities).
- General Topics • Finance • National Security & Intelligence • Crime • Emotions & Behaviors • Geography
Standard Taxonomies*
- People • Organizations • Companies • Geographic Places • Postal & Email Addresses • Phone Numbers • Social Media Mentions • Hashtags • Vehicles
Standard Entities*
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Studio Development Environment
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- Linguistic project builder for entity extraction and text classification
- Integrated with natural language understanding engine
- Inline knowledge graph browser with search capabilities
- Powerful editor with syntax highlighting and autocomplete with IntelliSense
- Text annotation tool for setting target entities and categories
- Quality dashboard for measuring precision and recall with respect to the targets defined in Studio
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Compendia’s NLP/NLU Powers Precise Content-Level Search
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Document Content Tags
Compendia’s NLP/NLU Powers Precise Content-Level Search
Taxonomy Categories
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Compendia at a glance: AI-ENABLED DATA ENRICHMENT & Search
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TAKE THIS TO ENABLE THISTURN IT INTO THIS HIGHLY UNSTRUCTURED DATA
RESEARCH ARCHIVE HIGHLY STRUCTURED DATA ENRICHED THROUGH
ML WORKFLOWS & NLP/NLU HIGHLY EFFICIENT, USABLE, SCALABLE DATA ARCHIVE;
DISCOVERABLE ENTITIES & RELATIONSHIPS
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Current Architecture UI / END USER CLIENTS WEB PERSISTENCE
Expert Studio
AWR UI
NNCompendia UI NNCompass UI Compendia Dashboard UI NiFi UI
Authentication AWR Logging Processor Synch PostgreSQL elastic NAS
Authentication Logging www Load Balancer www Load Balancer JETTY//: TOMCAT NiFi Logging Data Provenance Flow Files
Metadata normalization
Classification extractor
Custom data source API
De-duplication
Permission Persistence
Processors
Processors
Expert.nlp
elastic
3rd party CSV, xls, json, xml, Etc.…
Structured data, Metadata files
Formatted Results
Search Results
https – json doc text, awr session data,
normalized permissions
Metadata for Search
Store Doc text (Unencrypted)
Search Results
Text/ results
Get Doc text
Processing queue
Data sources and files
Documentum, Windchill, SharePoint, shared drives, PDFs, Word documents, PowerPoint, Etc.
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Deployment options
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Cloud native Multi-cloudhybrid On-premise
1 2 3 4
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Features Pro Core Basic Ingestion, Extraction, Preparation X X X AI-enabled Metadata Extraction X X X Precision Search & Recall X X X Analytics Orchestration X X X Export X X X NLU/NLP Enrichment X X Knowledge Graph X X Linguistic Project Development X Support Pro Core Basic Titan Omni-channel Support X X X
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COMPENDIA for research – LICENSING MODELS
Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.
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Demo
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Q & A
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AFRl/STO questions • Clearances • More than 20 cleared personnel
- Linguists, Engineers, Architects, Data Scientists, Executives
- Mostly TS/SCI, a few CNWDI
- Examples of Compendia running on closed systems • [Compendia] LANL, LLNL
- [NNData] AFRL, DARPA
- [ESUSA] FBI, DIA, DTRA, IC Agency
- Performance feedback & tuning • Direct access via cleared personnel
- In collaboration with cleared govt personnel
- Hybrid approach
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AFRl/STO questions
- Is Compendia already listed on some existing “approved government software list” or within some system’s “authority to operate?”
- ATO for Production instance of Compendia on classified at LANL is pending • [ESUSA]
- Prod on unclassified network for FBI
- Prod on SIPR for DTRA
- Prod on JWICS for an IC Customer
- Dev on JWICS for DIA ; Pending on Production/JWICS
- [NNData]
- Prod on SIPR for USAF
- Prod on NIPR pending with Army
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AFRl/STO questions • UDF…?
- The Unified Data Framework (UDF) is an extensible tuple store that allows Compendia to unify and normalize data across many different source types, including unstructured data, semi-structured and structured data. The normalized data can then exported or streamed into multiple formats and structures, including SQL, NoSQL, JSON, XML and Elastic indexes.
- Can Compendia work with (“learn”) from non-standard, non-proprietary data formats? • Yes, but this is done by training the OCR on the newly encountered format. The system doesn’t learn this
automatically.
- Describe your access framework… • Permissions Persistence Subsystem: Extracts, normalizes, synchronizes, and persists container / folder-level user
permissions.
- Designed to query each independent source system at a pre-determined interval to extract the containers / folders where each document resides, along with a list of the users who have permissions to either “read” or “browse” the associated content.
- If permissions are modified for a particular container / folder in the source system, those changes propagate to the entire Compendia system.
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AFRl/STO questions • User interface via browser…?
- Yes
- How are instantiation improvements made? For example, how do feedback loops operate when customizing linguistics packages (by incorporating existing customer ontologies, taxonomies and entities) from closed systems? • Our NLP/NLU is done manually (rules-based), meaning that there isn’t any ML that is feeding back to improve the linguistic project
automatically. There are several benefits to this approach:
- It’s fully ‘explainable’, not a black box
- It doesn’t rely on high volumes of data for training. Rather, a SME can begin developing an NLP project leveraging just their knowledge and expertise without the need to annotate data and build a large training set
- We have a set of off-the-shelf (OTS) projects ready to go, projects that have been developed over multiple years based on our expertise in government, finance, banking, insurance, pharma/life sciences, and publishing.
- These linguistic projects cover six different taxonomies for document categorization (Crime, Finance, Geography, General Topics, Emotions & Behaviors, National Security & Intelligence). We offer 61 entity extractions, covering standard entities (people, places, organizations, emails, phone numbers, etc.) as well as many custom extractions (MIDB codes, academic institutions, military facilities, etc.).
- Because our technology is rule-based, these OTS projects can serve as a solid starting point for most customers, with the ability for users to easily tune and build on these projects for their specific needs.
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AFRl/STO questions • Multiple copies of an existing database may exist on different systems… Consider the use case of
a large database that operates independently – BUT – that at regular intervals is “imported” into an even larger database operating at a different (e.g., ‘higher’) security level? Can the lower-level instantiation be imported and then “learn” the additional information?
- Compendia’s architecture enables rapid onboarding of new services from technology partners or customers / government entities, e.g., “Bring your own algorithm” capability.
- We can segment/compartmentalize to keep private/sensitive components separate, etc.
- Is a separate Compendia instantiation required? • Yes… Compendia is not a PL-5 / cross-boundary authorized system, however we enable MLS through the
extraction and normalization of Defense-specific classification markings into the ISM CAPCO standards.
- Are there additional costs for multiple instantiations? • Generally, yes but our licensing model is flexible…
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AFRl/STO questions • What other solutions are you aware of that you believe we should evaluate to insure best
value to the government? • Palantir – Focused on dynamic analysis leveraging real time data where precision search and recall is a feature set rather than a
focus
- IBM – DIY options but requires significant expertise to correctly chain together replicable functionality covered with Compendia
- Lucidworks – very commoditized and not likely to handle any custom requirements
- What do you believe we should know, regardless of whether we asked? What questions to you believe are “the right questions” for us to be asking, not only you, but others we encounter? • Compendia’s architecture allows for the integration or reuse of independently developed AI models, i.e., support for “Bring your
own Algorithm”
- Compendia’s available off the shelf linguistic packages and entity extractions that can be extended/adapted via a straightforward rules-based approach
- Compendia’s ability to classify items based on security markings is unique in the market
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Titan questions • Use cases / business problems?
- Types and volume of content, source systems?
- Nature of that content in terms of subject matter?
- Is there a need to do scanning or is your content already digital?
- Would AFRL provide all the hardware or would they want to acquire that via Titan?
- Any technical / OS level constraints? (e.g., red hat, docker, k8s, etc.)
- Do you anticipate integrating your own algorithms, projects, models, etc.?
- What would your deployment look like in terms of prod vs dev, unclass vs classified, etc., # of users, etc.
- Is an acquisition forthcoming? When?
RUBRIC
QUALITY OF RESPONSE NO RESPONSE POOR / UNSATISFACTORY SATISFACTORY GOOD EXCELLENT Content (worth a maximum of 50% of the total points) Zero points: Student failed to submit the final paper. 20 points out of 50: The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. 30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. 40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. 50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. Use of Sources (worth a maximum of 20% of the total points). Zero points: Student failed to include citations and/or references. Or the student failed to submit a final paper. 5 out 20 points: Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. 10 out 20 points: References to scholarly sources are occasionally given; many statements seem unsubstantiated. Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. 15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. 20 points: Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented. APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. Grammar (worth maximum of 20% of total points) Zero points: Student failed to submit the final paper. 5 points out of 20: The paper does not communicate ideas/points clearly due to inappropriate use of terminology and vague language; thoughts and sentences are disjointed or incomprehensible; organization lacking; and/or numerous grammatical, spelling/punctuation errors 10 points out 20: The paper is often unclear and difficult to follow due to some inappropriate terminology and/or vague language; ideas may be fragmented, wandering and/or repetitive; poor organization; and/or some grammatical, spelling, punctuation errors 15 points out of 20: The paper is mostly clear as a result of appropriate use of terminology and minimal vagueness; no tangents and no repetition; fairly good organization; almost perfect grammar, spelling, punctuation, and word usage. 20 points: The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. Structure of the Paper (worth 10% of total points) Zero points: Student failed to submit the final paper. 3 points out of 10: Student needs to develop better formatting skills. The paper omits significant structural elements required for and APA 6th edition paper. Formatting of the paper has major flaws. The paper does not conform to APA 6th edition requirements whatsoever. 5 points out of 10: Appearance of final paper demonstrates the student’s limited ability to format the paper. There are significant errors in formatting and/or the total omission of major components of an APA 6th edition paper. They can include the omission of the cover page, abstract, and page numbers. Additionally the page has major formatting issues with spacing or paragraph formation. Font size might not conform to size requirements. The student also significantly writes too large or too short of and paper 7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. 10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper. GET THIS PROJECT NOW BY CLICKING ON THIS LINK TO PLACE THE ORDER
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