1. Predicting P & R
by
Term Variability &
Word Sense Ambiguity
Farzaneh Sarafraz
5 March 2010

2. What do the following things
have in common?

3. EARRING STEMS

4. ARMY ENCAMPMENTS

5. STAFF
POSITION

6. BLOG
ARTICLE

7. TRADING STATION

10. Post

11. Polysemy – Word Sense Ambiguity
A

12. Term Variability
A
B
C

13. Term Variability Measure
# different words
V [class ]=
# all mentions
AABBBBCCAACAAAAAAAAAC

14. Word Entropy
#  word ,class  #  word , class
H [ word ]=− ∑  . log 2  
allclasses #  word ,* #  word ,* 
A

15. Class Entropy
# word , class
H [class ]= ∑ H [word ].
word ∈class # * , class
Class [Un]-characterisability / Confusion
# word , *−class # word , class
C [class]= ∑  . 
word ∈ class # word , * # * , class

16. Guess
Lexical Variability
R
AABBBBCCAAAAAAC
Class Characterisability
A P
B

17. Precision vs. Class Confusion
100
Protein Catabolism
90
Phosphorylation
80
70
Regulation
Gene Expression
60
Binding Positive Regulation
50 Localization
Negative Regulation
40
30
Transcription
20
10
0
0 0.05 0.1 0.15 0.2 0.25 0.3
Correlation = -0.72

18. Recall vs. Lexical Variability
120
100 Phosphorylation
Protein Catabolism
80 Gene Expression Localization
Transcription
60
40
Binding
Negative Regulation
20
Positive Regulation Regulation
0
0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
Correlation = -0.68

19. Results For Other Teams
Average precision vs. class
Average recall vs. variability confusion
Correlation = -0.82 Correlation = 0.03

20. Ideas, please?

21. Information Extraction
K J G T C S H U A G Y X W.

22. Information Extraction
K J G T C S H U A G Y X W.

23. Information Extraction
K J G T C S H U A G Y X W.

24. Information Extraction
K J G T C S H U A G Y X W.