Difference between revisions of "Warlpiri/Transducer"
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The code can be found here: [https://github.swarthmore.edu/tjones5/ling073-wbp] | The code can be found here: [https://github.swarthmore.edu/tjones5/ling073-wbp] | ||
| − | ==Notes== | + | ==Analyzer Evaluation== |
| + | ===Notes=== | ||
There are still several tests that don't pass, because I have not yet added the necessary forms to the transducer. For example: | There are still several tests that don't pass, because I have not yet added the necessary forms to the transducer. For example: | ||
*Grammatical and Derivational Case Suffixes | *Grammatical and Derivational Case Suffixes | ||
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I also added the common word "manu", which means "and". By adding "{{morphTest|manu{{tag|cnjcoo}}|manu}}" to the transducer, coverage went from 7.91% to 9.01%. | I also added the common word "manu", which means "and". By adding "{{morphTest|manu{{tag|cnjcoo}}|manu}}" to the transducer, coverage went from 7.91% to 9.01%. | ||
| − | ==Evaluation== | + | ===Evaluation=== |
Total number of stems in the transducer: 26 | Total number of stems in the transducer: 26 | ||
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Number of passing tests in commonwords.yaml: 1 / 4 | Number of passing tests in commonwords.yaml: 1 / 4 | ||
| + | |||
| + | ==Generator Evaluation== | ||
| + | |||
| + | ===Initial evaluation of morphological generation=== | ||
| + | |||
| + | Number of passing analyzer tests in wbp.yaml: 62 / 120 | ||
| + | |||
| + | Current coverage over the combined corpus: 9.01% | ||
| + | |||
| + | Number of passing generator tests in wbp.yaml: 62 / 160 | ||
| + | |||
[[Category:Sp17_Transducers]] | [[Category:Sp17_Transducers]] | ||
Revision as of 16:58, 21 February 2017
Contents
Code
The code can be found here: [1]
Analyzer Evaluation
Notes
There are still several tests that don't pass, because I have not yet added the necessary forms to the transducer. For example:
- Grammatical and Derivational Case Suffixes
- Clitics and Particles
- Directional Endings on verbs
- Double Dative
- Specific nominal, auxiliary, preverb, and verb examples
Many of these should be fairly straight-forward to implement.
There's one failed test within the Tesnse-Like Auxiliaries, the rest of which all pass. This is "rna-jana", for which the auxiliary in the perfect tense is null. Given that in all other tenses, "rna" and "jana" are attached with dashes, the generated form for this instance is "-rna-jana". I may have to implement Spell Relax to deal with the presence/absence of the dashes across the corpus.
I also added the common word "manu", which means "and". By adding "manu<cnjcoo> ↔ manu" to the transducer, coverage went from 7.91% to 9.01%.
Evaluation
Total number of stems in the transducer: 26
Current coverage over the combined corpus: 9.01%
Top unknown words:
| word | number of instances |
|---|---|
| nyayirni | 13 |
| kurlu | 13 |
| ju | 12 |
| R | 11 |
| lu | 11 |
| jarra | 10 |
| lku | 10 |
| mani | 9 |
| yapa | 9 |
| a | 9 |
| kurra | 9 |
| war | 9 |
| jana | 9 |
| r | 8 |
| juku | 8 |
| mar | 8 |
| Jarra | 8 |
| rla | 8 |
| kapu | 7 |
Number of passing tests in wbp.yaml: 62 / 120
Number of passing tests in commonwords.yaml: 1 / 4
Generator Evaluation
Initial evaluation of morphological generation
Number of passing analyzer tests in wbp.yaml: 62 / 120
Current coverage over the combined corpus: 9.01%
Number of passing generator tests in wbp.yaml: 62 / 160
