Case 1: Part Of Speech Tagging
Example of what machine has LEARNT
(Extracted from Geniatagger rules)
| NNP | pre1_M | -0.050704 |
| NNP | suf2_r. | 2.551747 |
| NNP | pre2_Mr | 1.559546 |
| NNP | suf3_Mr. | 0.506174 |
| NNP | pre3_Mr. | 0.506174 |
| NNP | P+2_VBZ | 0.464316 |
| NNP | W-1_Mr. | 4.841904 |
| NNP | W+1_is | 1.599005 |
| NNP | P+2_NN | -0.658921 |
| VBZ | W0_is | 9.915353 |
| VBZ | suf1_s | 5.528909 |
| VBZ | pre1_i | 0.294491 |
| VBZ | suf2_is | -1.231646 |
| VBZ | pre2_is | 0.351233 |
| VBZ | P+2_IN | 0.142350 |
| NN | W-1_is | -0.321235 |
| NN | W+1_of | 3.437618 |
| NN | suf1_n | 1.245612 |
| NN | pre1_c | 5.185552 |
| NN | suf2_an | 0.019884 |
| NN | pre2_ch | 0.228939 |
Invited Speech at NWAI-MLcontest-4th July2006