Table 3 Contribution of linguistic features. Results from various combinations of types of linguistic features, as described in Features section, combined using Vector Space Model learning algorithm. LC = Local Collocations, SB = Salient Bigrams and U = Unigrams.
From: Disambiguation of biomedical text using diverse sources of information
| Â | Features | |||||
|---|---|---|---|---|---|---|
Data sets | LC | SB | U | SB+U | LC+SB | LC+U |
All words | 79.2 | 82.0 | 86.9 | 85.9 | 86.3 | 86.9 |
Joshi subset | 72.6 | 74.4 | 81.6 | 82.3 | 81.0 | 82.0 |
Leroy subset | 66.2 | 66.9 | 76.7 | 77.5 | 76.5 | 77.3 |
Liu subset | 75.7 | 76.2 | 83.4 | 84.3 | 82.7 | 83.9 |
Common subset | 69.6 | 77.7 | 79.3 | 79.1 | 77.6 | 78.8 |