A presentation by UVA Data Science Institute MSDS 2019 students Sakshi Jawarani, Murugesan Ramakrishnan, and Varshini Sriram, advised by MSDS Program Director and professor Rafael Alvarado, at the 2019 Tom Tom Applied Machine Learning Conference in Charlottesville, VA.

1. Collective Biographies Of Women
A Deep Learning Approach to
Paragraph Annotation
Members: Murugesan Ramakrishnan, Sakshi Jawarani, Varshini Sriram
Advisor: Prof. Rafael Alvarado
Sponsored by: Institute for Advanced Technology in the Humanities

2. Introduction
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●

3. Sample BESS Annotated Paragraph

4. Problem Statement & Importance

5. Approach
MACHINE LEARNING
MODELS
DEEP LEARNING
MODELS
PRE-PROCESSING FEATURE ENGINEERING

6. Data pre-processing
●
●
Fuzzy
Algorithm
Persona
names
Mapping
files
Fuzzy Algorithm

7. Feature Extraction

8. Narrative Structure: Sister Dora

9. Color Stage of Life
Before
Beginning
Middle
Culmination
End
Lola Montez - Stages of Life

10. Unseen Paragraphs
Classification
Model
Ex. Marriage /NOT Marriage
Marriage
NOT Marriage
Machine Learning Classification

11. Classification Models
Sentiments
& Emotions
Named
Entities
Topics
Word
Vectors
Logistic
Regression
Random
Forest
Support
Vector
Features
Machine
Learning
Models

12. Language Models

13. Model Results
Model F1 Score
Logistic Regression 0.15
Random Forest 0.07
SVM 0.03
Language Models 0.27

14. Problems & Solution - Clustering
●
●
●
Persona
191
Topos 173
Discourse
191
Events
2102

15. Events
2102 Categories
-> 50 Clusters
Illness, Mental
Illness, family
Illness, persona’s
Hospital
Disability
Addiction
…..
Gardening
Farmer
Farming
Animal, Farm
Agricultural
Cottage
…..
Brother
Mother
Stepmother
Stepfather
Aunt
Sisters
Son/Sons
…..

16. Deep Learning Approach

17. Conclusion
● Clustering reduced the cardinality of the classes dramatically and helped
obtain better results.
● We strongly recommend these modified categories for further paragraph
annotation for the CBW project
● Our results will be used to further annotate 13,000 chapters of women
biographies

18. Thank you