1 files changed, 0 insertions, 39 deletions
diff --git a/examples/trigram/Trigram.gf b/examples/trigram/Trigram.gf
deleted file mode 100644
index 0ad99b0bf..000000000
--- a/examples/trigram/Trigram.gf
+++ /dev/null
@@ -1,39 +0,0 @@
-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