| Safe Haskell | None |
|---|---|
| Language | Haskell98 |
NLP.Concraft.DAGSeg
Description
Top-level module adated to DAGs, guessing and disambiguation.
Synopsis
- data Concraft t = Concraft {}
- saveModel :: (Ord t, Binary t) => FilePath -> Concraft t -> IO ()
- loadModel :: (Ord t, Binary t) => (Tagset -> t -> Tag) -> (Tagset -> t -> Tag) -> FilePath -> IO (Concraft t)
- type Anno a b = DAG () (Map a b)
- findOptimalPaths :: Ord t => Anno t Double -> [[(EdgeID, Set t)]]
- disambPath :: Ord t => [(EdgeID, Set t)] -> Anno t Double -> Anno t Bool
- guessMarginals :: (Word w, Ord t) => Guesser t Tag -> Sent w t -> Anno t Double
- disambMarginals :: (Word w, Ord t) => Disamb t -> Sent w t -> Anno t Double
- disambProbs :: (Word w, Ord t) => ProbType -> Disamb t -> Sent w t -> Anno t Double
- guessSent :: (Word w, Ord t) => Int -> Guesser t Tag -> Sent w t -> Sent w t
- guess :: (Word w, Ord t) => Int -> Guesser t Tag -> Sent w t -> Anno t Double
- tag :: (Word w, Ord t) => Int -> Concraft t -> Sent w t -> Anno t Double
- prune :: Double -> Concraft t -> Concraft t
Model
Concraft data.
saveModel :: (Ord t, Binary t) => FilePath -> Concraft t -> IO () Source #
Save model in a file. Data is compressed using the gzip format.
Arguments
| :: (Ord t, Binary t) | |
| => (Tagset -> t -> Tag) | Guesser simplification function |
| -> (Tagset -> t -> Tag) | Disamb simplification function |
| -> FilePath | |
| -> IO (Concraft t) |
Load model from a file.
Annotation
type Anno a b = DAG () (Map a b) Source #
DAG annotation, assignes b values to a labels for each edge in the
graph.
Best paths
findOptimalPaths :: Ord t => Anno t Double -> [[(EdgeID, Set t)]] Source #
Find all optimal paths in the given annotation. Optimal paths are those which go through tags with the assigned probability 1. For a given chosen edge, all the tags with probability 1 are selected.
disambPath :: Ord t => [(EdgeID, Set t)] -> Anno t Double -> Anno t Bool Source #
Make the given path with disamb markers in the given annotation and produce a new disamb annotation.
Marginals
guessMarginals :: (Word w, Ord t) => Guesser t Tag -> Sent w t -> Anno t Double Source #
Determine marginal probabilities corresponding to individual tags w.r.t. the guessing model.
disambMarginals :: (Word w, Ord t) => Disamb t -> Sent w t -> Anno t Double Source #
Determine marginal probabilities corresponding to individual tags w.r.t. the guessing model.
disambProbs :: (Word w, Ord t) => ProbType -> Disamb t -> Sent w t -> Anno t Double Source #
Determine probabilities corresponding to individual tags w.r.t. the guessing model.
Tagging
guessSent :: (Word w, Ord t) => Int -> Guesser t Tag -> Sent w t -> Sent w t Source #
Extend the OOV words with new, guessed interpretations.
Determine marginal probabilities corresponding to individual tags w.r.t.
the guessing model and, afterwards, trim the sentence to keep only the k
most probably labels for each OOV edge. Note that, for OOV words, the entire
set of default tags is considered.
guess :: (Word w, Ord t) => Int -> Guesser t Tag -> Sent w t -> Anno t Double Source #
Perform guessing, trimming, and finally determine marginal probabilities corresponding to individual tags w.r.t. the guessing model.
tag :: (Word w, Ord t) => Int -> Concraft t -> Sent w t -> Anno t Double Source #
Perform guessing, trimming, and finally determine marginal probabilities corresponding to individual tags w.r.t. the disambiguation model.