This document summarizes research on classifying tables in scientific documents. It introduces an IMRAD-based taxonomy that categorizes tables by their location in the introduction, methods, results or discussion sections. It also presents a fine-grained taxonomy that categorizes tables by their contents and purpose (e.g. definition, statistics, survey tables). The researchers classified 2500 tables using these taxonomies and machine learning algorithms, achieving high accuracy rates for IMRAD classification (97%) and some fine-grained types like results tables (100%). Future work will explore additional features from table layouts and contents.

1. Scientific Table Type
Classification in Digital Library
Seongchan Kim, Keejun Han, Ying Liu D
ept. of Knowledge Service Engineering K
AIST, Korea
Soon Young Kim
Dept. of Overseas Information
KISTI, Korea
Sept. 6, 2012 (3:35-3:55)

2. Outline
Introduction
Table Type Taxonomy
IMRAD-Base
d Fine-Graine
d
Table Type Distribution
Classification
Conclusion

3. Outline
Introduction
Table Type Taxonomy
IMRAD-Base
d Fine-Graine
d
Classification
Conclusion

4. 4 / 20
Introduction
• Are there any special types of tables in papers in scientific papers?
If yes? What are they?

5. 5 / 20
Introduction
• Are there any special types of tables in papers in scientific papers?
If yes? What are they?

6. Outline
Introduction
Table Type Taxonomy
IMRAD-Base
d Fine-Graine
d
Classification
Conclusion

7. 7 / 20
Table Type Taxonomy
2,500 tables randomly from 25 randomly selected scie
ntific journals published by Springer from 2006 to 2010
Biomedical and Life Science, Chemistry and Materials S
cience, Computer Science, Electrical Engineering, and
Medicine
TableSeer
We found
 IMRAD-based table taxonomy
 Fine-grained table taxonomy

8. IMRAD-Based Table Taxonomy
Considering the structural position of tables within a
document
Table type is simply decided by the location of the
table
 E.g) if a table is in the introduction part of the paper
 Introduction table Other functions
Scientific
Table
Introduction
Table
Method
Table
Resul
t Tabl
e
Discussio
n Table
8 / 20

9. Fine-Grained Table Taxonomy
Table type is decided by table contents and purposes
Scientific
Table
Definition
Table
Statistic
Table
Survey
Table
Example
Table
Procedure
Table
Experiment
Setting T
able
Experiment
Result T
able
9 / 20

10. Fine-Grained Table Taxonomy
Definition Tables
 Consist of defining terms
and their explanations
 Usually appears before
the experiment
10 / 20

11. Fine-Grained Table Taxonomy
Statistics/Distribution
Tables
 Common statistical or
distribution data
 Not related with the c
urrent experiment being c
arried out in the paper
11 / 20

12. Fine-Grained Table Taxonomy
Survey Question/Result Table
 Contain questionnaires of the
those questionnaires
survey and the results of
12 / 20

13. Fine-Grained Table Taxonomy
Example Table
 Show instances that introduce and emphasize something
that needs to be explained clearly
13 / 20

14. Fine-Grained Table Taxonomy
Procedure Tables
 Describe the sequence
,
methods
step, flow, or schedule of the
14 / 20

15. Fine-Grained Table Taxonomy
Experiment Setting Tables
 Describe items required for the experiment
 configurations, parameters, data, apparatus, etc.
15 / 20

16. Fine-Grained Table Taxonomy
Experiment Setting Tables
 accompanied with a summary describing the output of the
experiment
 Some are shown comparing the other results
16 / 20

17. Table Type Distribution
By IMRAD Taxonomy
3.7
%
20.2
%
74.6
%
1.5
%
Introduction
Methods
Result
s
Discussion
2.9% 5.0%
0.3%
3.3%
1.6%
14.9%
72.0%
Definition St
atistics Surv
ey Example
Procedure
Exp. Setting
Exp. Result
17 / 20
By Fine-Grained Taxonomy
Annotation
 2,380 and 2,324 tables that had agreed on labeling from
more that two annotators out of 2,500 tables
 Inter-Annotator Agreement
• = 0.64 for IMRAD annotation
• = 0.53 for fine-grained annotation

18. Outline
Introduction
Table Type Taxonomy
IMRAD-Base
d Fine-Graine
d
Classification
Conclusion

19. 19 / 20
Experiment
A preliminary classification
 Only textual feature from Table
• Table Caption
• Table Reference Text
 Textual Information obtained from metadata of TableSeer [ ]
Settings
 DataSet
• 2,380 tables for IMRAD classification
• 2,324 tables for fine-grained classification
 10-fold Cross validation
 SVM and Decision Tree in Weka toolkit (default setting)

20. Experiment
20 / 20
C1: T1, T
2
C2: T3, T
4
W1 : appears T1 and T2
W2 : appears T1 and T3

21. 21 / 20
Experiment
Result
Performance of IMRAD Classification by Features
Performance of Fine-grained Classification by Features
Features SVM Decision Tree
P R F P R F
Cap.(Baseline) 0.836 0.506 0.543 0.947 0.550 0.792
Ref. 0.875 0.705 0.761 0.930 0.639 0.73
Cap.+Ref. 0.967 0.784 0.866 0.938 0.746 0.831
Features SVM Decision Tree
P R F P R F
Cap.(Baseline) 0.627 0.333 0.397 0.522 0.271 0.302
Ref. 0.707 0.615 0.649 0.790 0.673 0.716
Cap.+Ref. 0.701 0.657 0.668 0.764 0.62 0.671

22. 22 / 20
Experiment
Result
Performance of IMRAD Classification by Types
Type SVM Decision Tree
P R F P R F
Introduction 0.968 0.6 0.741 0.907 0.68 0.777
Methods 0.901 0.996 0.943 0.912 0.992 0.95
Results 1 1 1 1 1 1
Discussion 1 0.543 0.704 0.933 0.314 0.44
Macro Avg. 0.967 0.784 0.866 0.938 0.746 0.831
Micro Avg. 0.977 0.976 0.973 0.973 0.975 0.972

23. 23 / 20
Experiment
Result
Performance of Fine-grained Classification by Types
Type SVM Decision Tree
P R F P R F
Definition 0.689 0.609 0.646 0.837 0.522 0.643
Statistics 0.699 0.879 0.779 0.898 0.681 0.775
Survey 0 0 0 0 0 0
Example 0.716 0.725 0.72 0.875 0.525 0.656
Procedure 0.9 0.486 0.632 1 0.649 0.787
Exp. Setting 0.905 0.9 0.902 0.74 0.963 0.837
Exp. Result 1 1 1 1 1 1
Macro Avg. 0.701 0.657 0.668 0.764 0.62 0.671
Micro Avg. 0.947 0.947 0.946 0.94 0.936 0.987

24. Outline
Introduction
Table Type Taxonomy
IMRAD-Base
d Fine-Graine
d
Classification
Conclusion

25. 25 / 20
Conclusion
Introduced our study of table types and classifications
in scientific papers
 IMRAD-based Taxonomy
 Fine-Grained Taxonomy
Future Work
 Developing various features from table layout and contents