1. TEAM 2 EMERGING INFORMATION TECHNOLOGIES I
TEAM MEMBERS: Lisa Ellrodt, Tonya Fields, Ion Freeman, Ashley Haigler and Suzanna Schmeelk (Pace University, USA)
COURSE: DCS-860A
PROFESSORS: Dr. Charles Tappert and Dr. Tilak Agerwala
DATE: SEPTEMBER 29, 2018

2. Emerging Information Technology - Machine Learning
“Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. “ The SAS Institute
Some Examples: Netflix : based on our viewing habits suggests new shows
Pandora/Apple Music/Google Play Music: Bases on our listening habits suggests new music
Waze: Optimizes driving experience based on known road data
Amazon: offers recommendations based on our shopping habits

3. Machine Learning Video
IBM Watson Machine Learning:
Example of Machine Learning:
and Softbank Robotics, Inc

4. PAPER TOPIC: BACKGROUND
Fall 2017, characterize DPS dissertations produced in program
Worked to hand classify the papers found standardization was challenging
Resolved by using machine learning to cluster the dissertation abstracts
Original approach: TF-IDF (Term Frequency - Inverse Document Frequency)
Spring 2018, we tried 4-5 different algorithms in Weka and IBM BlueMix to see differences
Fall 2018, we plan to clean the data described in the following pages

5. WORK TO DATE – Publishing Teamwork Research
Fall 2017 paper was published in Pace Research Day
Spring 2018, after Pace Research Day we improved the thrust of the paper; it was accepted into:
IEEE-FIE 2018 (48th Annual IEEE-Frontiers in Education) San Jose, CA
Summer 2018 our paper included the works from IBM and Weka
It is under review at IEEE-ICMLA 2018 (17th IEEE International Conference on Machine Learning and Applications)

6. DISTANT FUTURE PLANS DETAILS (Which Could be Dissertation)
First, refine the methodology on the dissertation database.
Apply method to other databases to show that it generalizes
Categorizes the variety research papers
Example, apply to 5-10 years of articles from several journals

7. PLANS FOR FALL 2018 Redo the first study
Focus on data preparation which is usually the most tedious and important step in data analytics
Data preparation steps:
Tokenization – separate words from the text
Case folding – reduce all letters to lowercase
Lemmatization – find dictionary base forms
Stemming (e.g., Porter’s stemming algorithm) - similar to lemmatization but dictionary not required
Eliminate stop words - such as the, a, of, and
Eliminate domain-specific stop words - not useful for the study – in this case “study”, “dissertation”, etc.
Other Considerations
Emphasis: Add to the abstract words the words from the dissertation titles
doubling or tripling the dissertation title words, to increase the bag of words from each dissertation.
Outliers: Removing might improve cluster distributions

8. Background Information on Data Preparation
Chapter 9 in
Related slides at

9. Tokenization Breaks up a sequence of strings into pieces called tokens
Words
Keywords
Phrases
Symbols
Other Elements
Tokens can be individual words, phrases or whole sentences
Tokens can only be made up of all alpha characters, alphanumeric characters or numeric characters
Tokens are separated by whitespace, punctuation marks or line breaks
White space or punctuation marks may or may not be included depending on the need
All characters within contiguous strings are part of the token
In the process of tokenization, characters like punctuation marks are discarded
The tokens become the input for process like parsing and text mining
Tokenization is used in computer science
Plays a large part in the process of analytics

10. Tokenization

11. Case folding – reduce all letters to lowercase

12. Lemmatization – find dictionary base forms
Determining the root stem of the words
Notes about lemmatization:
Lemmatize takes a part of speech parameter, "pos."
If not supplied, the default is "noun." This means that an attempt will be made to find the closest noun, which can create trouble for you.
Must watch out for this!
Show video on below?
Reference: nltk-tutorial/

13. Lemmatization

14. Stemming (e.g., Porter’s stemming algorithm) - similar to lemmatization but dictionary not required

15. Eliminate stop words, such as the, a, of,

16. FUTURE WORK FOR THIS SEMESTER
Eliminate domain-specific stop words not useful for the study – in this case “study”, “dissertation”, etc.
FUTURE WORK FOR THIS SEMESTER

17. Good news! We’ve started our paper already! Team: Lisa Ellrodt
Action Items
Good news! We’ve started our paper already!
Team:
Lisa Ellrodt
Tonya Fields
Ion Freeman
Ashley Haigler
Suzanna Schmeelk

18. Need a new venue for publication Research Questions:
Conclusion
Need a new venue for publication
Research Questions:
Will we see differences in clustering based on data preparation?
TF-IDF, IBM, Spark, PiTorch, TensorFlow
Identify new clustering algorithms
Hyperparameters