CG hybrid tagging
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Tagging[edit]
The tagger is more robust against missing ambiguity sets. If it encounters a new ambiguity set it picks the a) smallest b) most frequent of them (in that order). This using of the "nearest" ambiguity set is used in other places too.
Apart from feeding in ambiguity sets as is after CG as is the current common practice before this work, tagging using a mix of untagged and CG, discarding CG analysis in favour of untagged analysis when there is any ambiguity.
Invasive...
Tagger training[edit]
Both supervised and unsupervised:
Mode Model part |
0 | 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|
| Ambiguity classes | Dictionary | CG tagged | Dictionary | Dictionary | CG tagged + trimming |
| Ambiguity class frequency | Untagged | CG tagged | Untagged | Untagged | CG tagged |
| Corpus | Untagged | CG tagged | CG tagged (nearest) | Mix | CG tagged (nearest) |
Note that in the case of supervised training the corpus is used in conjunction with the tagged corpus.
Results[edit]
Compare with Comparison of part-of-speech tagging systems.
