This system addresses the verb sense disambiguation (VSD) problem, a sub-problem of word sense disambiguation (WSD), for English. It predicts the sense that a verb features in a given sentence, from among other, potential different meanings.
This system was developed as a part of Semantic Role Labeling system.
Here is the performance of this system after the most recent training:
Label | Correct | Excess | Missed | Precision | Recall | F1 |
---|---|---|---|---|---|---|
01 | 4230 | 205 | 113 | 95.38 | 97.4 | 96.38 |
02 | 378 | 88 | 147 | 81.12 | 72 | 76.29 |
03 | 178 | 30 | 60 | 85.58 | 74.79 | 79.82 |
04 | 51 | 16 | 14 | 76.12 | 78.46 | 77.27 |
05 | 16 | 10 | 16 | 61.54 | 50 | 55.17 |
06 | 10 | 8 | 10 | 55.56 | 50 | 52.63 |
07 | 3 | 2 | 3 | 60 | 50 | 54.55 |
08 | 6 | 4 | 0 | 60 | 100 | 75 |
09 | 3 | 2 | 3 | 60 | 50 | 54.55 |
10 | 0 | 3 | 3 | 0 | 0 | 0 |
11 | 10 | 1 | 0 | 90.91 | 100 | 95.24 |
12 | 3 | 1 | 3 | 75 | 50 | 60 |
13 | 1 | 2 | 1 | 33.33 | 50 | 40 |
14 | 5 | 1 | 3 | 83.33 | 62.5 | 71.43 |
15 | 0 | 2 | 0 | 0 | 0 | 0 |
16 | 0 | 1 | 0 | 0 | 0 | 0 |
17 | 1 | 1 | 0 | 50 | 100 | 66.67 |
18 | 0 | 1 | 0 | 0 | 0 | 0 |
19 | 0 | 0 | 1 | 0 | 0 | 0 |
20 | 0 | 0 | 1 | 0 | 0 | 0 |
All | 4895 | 378 | 378 | 92.83 | 92.83 | 92.83 |
While you can dig through the code to use it directly, we suggest you use it through our pipeline. More details in pipeline's instructions.
If you use this system, and want to give credits to our system, please cite the following work:
@inproceedings{PRYZT04,
author = {V. Punyakanok and D. Roth and W. Yih and D. Zimak and Y. Tu},
title = {Semantic Role Labeling via Generalized Inference over Classifiers Shared Task Paper},
booktitle = {CoNLL},
pages = {130--133},
year = {2004},
comment = {Semantic Parsing; Structure Learning with Expressive Constraints; Constraint Optimization; Integer Linear Programming},
}