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authorjohn.j.camilleri <john.j.camilleri@chalmers.se>2013-09-16 07:17:27 +0000
committerjohn.j.camilleri <john.j.camilleri@chalmers.se>2013-09-16 07:17:27 +0000
commitf5461eb3d4eb2605b546a4ed202c12bcdaa1f4e4 (patch)
tree946c9e8542b8e8271b6b529a95c0400fa6613cb4 /examples/trigram/Trigram.gf
parent8e1c6cca407c82fc09569d80c231b8d256735989 (diff)
Remove contribs and examples
Everything has now been moved to a separate repository at https://github.com/GrammaticalFramework/gf-contrib The contents of the examples folder are build during SetupWeb
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-abstract Trigram = {
-
-cat
- -- A sentence
- S ;
-
- -- A lexicon is a set of 'Word's
- Word ;
-
- -- All N-gram instances seen in the corpus are abstract syntax constants
- Unigram (a : Word) ;
- Bigram (a,b : Word) ;
- Trigram (a,b,c : Word) ;
-
- -- A text is a sequence words where the sequence is indexed by the last two tokens
- Seq (a,b : Word) ;
-
- -- The estimated probability of the trigram 'a b c' is the total probability of all
- -- trees of type Prob a b c.
- Prob (a,b,c : Word) ;
-
-data
- sent : ({a,b} : Word) -> Seq a b -> S ;
-
- -- Here we construct sequence by using nil and cons. The Prob argument ensures
- -- that the sequence contains only valid N-grams and contributes with the right
- -- probability mass
- nil : (a,b,c : Word) -> Prob a b c -> Seq b c ;
- cons : ({a,b} : Word) -> Seq a b -> (c : Word) -> Prob a b c -> Seq b c ;
-
- -- Here we construct probabilities. There are two ways: by trigrams, by bigrams and
- -- by unigrams. Since the trigramP, bigramP, unigramP functions have some associated
- -- probabilities as well this results in linear smoothing between the unigram, bigram
- -- and trigram models
- trigramP : ({a,b,c} : Word) -> Trigram a b c -> Prob a b c ;
- bigramP : ({a,b,c} : Word) -> Bigram a b -> Bigram b c -> Prob a b c ;
- unigramP : ({a,b,c} : Word) -> Unigram a -> Unigram b -> Unigram c -> Prob a b c ;
-
-} \ No newline at end of file