Tabular Data Extraction Epidemiology Table Classification and - - PowerPoint PPT Presentation

Tabular Data Extraction

Epidemiology Table Classification and Factor Alignment

Garrick Sherman

Last semester...

• Worked with Dr. Andrew Leakey

○ Plant biologist ○ The effects of carbon dioxide on photosynthesis

• Data is “locked away”
• Goals

○ Extract data from articles ○ Keep data associated with articles ○ Add structure to data

Last semester...

• Look for a set of search terms
• Parse HTML tables into CSV files

○ Also extract table captions

• Identify columns, “subtables,” and captions

about the search terms

Last semester...

• Column-based

○ 53 columns extracted ○ Recall: 0.1130 or 0.6279 ○ Precision: 0.3774 or 0.5094

• Subtables

○ 23 extracted ○ Recall: 0.1356 ○ Precision: 0.3158

This semester...

• Epidemiology journals

○ 11 high impact breast cancer journals ■ e.g. British Medical Journal, Cancer, International Journal of Breast Cancer, etc.

• Classify table as containing summary

sample characteristics

• Align factors

○ e.g. “Marital Status” and “Married”

{"Age at diagnosis (years):"=> {"<40"=>["9 (146)", "5 (12)"], "40-49"=>["26 (437)", "29 (71)"], "50-59"=>["37 (631)", "39 (94)"], "≥60"=>["29 (500)", "27 (66)"]}, "Marital status:"=> {"Living with partner"=>["79 (180)"], "Living alone"=>["21 (48)"]}, "Metropolitan classification:"=> {"Metropolitan area"=>["59 (1001)", "49 (119)"], "Non-metropolitan area"=>["41 (711)", "51 (124)"]}, ….

This semester...

• Goal:

○ Automated metadata extraction ○ Faceted search ■ Find studies of related populations

Dataset

• First table

○ ~1,500 first tables ○ Train: 1,001 ○ Test: 497

• NXML format
• Fresh codebase

○ But same table parsing approaches

Training

• Manual annotation

○ Classify based on first 10 lines (or more, if needed) and caption ○ Final tally: ■ 41.36% sample characteristics ■ 58.64% other

• Would certainly be improved with domain

expertise

Classification

• Information gain

○ Tokens from factor and options

Classification

• Test results:

○ 177 predicted positive ■ Random sample of 50 ■ Precision: 85.71% ○ 300 predicted negative ■ Random sample of 50 ■ Precision: 76.00%

Factor Alignment

• Alignment approaches

○ Literal ○ Percentage-based ○ Name-inclusive

• Evaluation

○ Choose 10 randomly, calculate precision ○ Report average precision ○ Has some drawbacks

Factor Alignment

• #1

○ Histology (N = 20) ■ Ductal,Lobular,Other ○ Morphological type ■ Ductal,Lobular,Other,Unknown ○ Histological type ■ Ductal,Lobular,Other,NA

• #2

○ Histological type ■ Ductal,Lobular,Ductulolobular,Medullary ○ Histology ■ Ductal,Lobular,Medullary

Factor Alignment

• Results

○ Literal: 0.9167 ○ Percentage: 0.8624 ○ Name-based: 0.9500

Conclusion

• Naive Bayes classifier works well because

data is independent

• Simple methods of factor alignment are

effective

• Automated approaches can help resolve

table structure and contents

• Potential applications for faceted search