concraft-0.4.0: src/NLP/Concraft/Disamb.hs
{-# LANGUAGE RecordWildCards #-}
module NLP.Concraft.Disamb
(
-- * Model
Disamb (..)
, Tier.CRF ()
-- * Tiers
, P.Tier (..)
, P.Atom (..)
, P.tiersDefault
-- * Disambiguation
, disamb
, disambSent
, disambDoc
-- * Training
, TrainConf (..)
, train
) where
import Control.Applicative ((<$>), (<*>))
import Data.Maybe (fromJust)
import Data.List (find)
import Data.Foldable (Foldable, foldMap)
import Data.Binary (Binary, put, get)
import qualified Data.Set as S
import qualified Data.Map as M
import qualified Data.Vector as V
import qualified Data.Text.Lazy as L
import qualified Data.Text.Lazy.IO as L
import qualified Control.Monad.Ox as Ox
import qualified Data.CRF.Chain2.Generic.External as CRF
import NLP.Concraft.Schema hiding (schematize)
import qualified NLP.Concraft.Morphosyntax as Mx
import qualified NLP.Concraft.Format as F
import qualified NLP.Concraft.Disamb.Tiered as Tier
import qualified NLP.Concraft.Disamb.Positional as P
import qualified Data.Tagset.Positional as TP
import qualified Numeric.SGD as SGD
-- | Schematize the input sentence with according to 'schema' rules.
schematize :: Schema t a -> Mx.Sent t -> CRF.Sent Ob t
schematize schema sent =
[ CRF.mkWord (obs i) (lbs i)
| i <- [0 .. n - 1] ]
where
v = V.fromList sent
n = V.length v
obs = S.fromList . Ox.execOx . schema v
lbs i = Mx.interpsSet w
where w = v V.! i
-- | A disambiguation model.
data Disamb = Disamb
{ tagset :: TP.Tagset
, tiers :: [P.Tier]
, schemaConf :: SchemaConf
, crf :: Tier.CRF Ob P.Atom }
instance Binary Disamb where
put Disamb{..} = put tagset >> put tiers >> put schemaConf >> put crf
get = Disamb <$> get <*> get <*> get <*> get
-- | Unsplit the complex tag (assuming, that it is one
-- of the interpretations of the word).
unSplit :: Eq t => (r -> t) -> Mx.Word r -> t -> r
unSplit split' word x = fromJust $ find ((==x) . split') (Mx.interps word)
-- -- | CRF training function.
-- type TrainCRF o t c
-- = IO [CRF.SentL o t] -- ^ Training data 'IO' action
-- -> Maybe (IO [CRF.SentL o t]) -- ^ Maybe evalation data
-- -> IO c -- ^ Resulting model
--
-- -- | CRF tagging function.
-- type TagCRF o t = CRF.Sent o t -> [t]
-- | Perform context-sensitive disambiguation.
disamb :: Disamb -> Mx.Sent F.Tag -> [F.Tag]
disamb Disamb{..} sent
= map (uncurry embed)
. zip sent
. Tier.tag crf
. schematize schema
. Mx.mapSent split
$ sent
where
schema = fromConf schemaConf
split = P.split tiers . TP.parseTag tagset
embed = unSplit split
-- | Tag the sentence.
disambSent :: F.Sent s w -> Disamb -> s -> s
disambSent F.Sent{..} dmb sent =
flip mergeSent sent
[ select wMap orig
| (wMap, orig) <- zip
(doDmb sent)
(parseSent sent) ]
where
F.Word{..} = wordHandler
doDmb orig =
let xs = map extract (parseSent orig)
in map (uncurry mkChoice) (zip xs (disamb dmb xs))
mkChoice word x = Mx.mkWMap
[ if x == y
then (x, 1)
else (y, 0)
| y <- Mx.interps word ]
-- | Disambiguate document.
disambDoc :: Functor f => F.Doc f s w -> Disamb -> L.Text -> L.Text
disambDoc F.Doc{..} dmb =
let onSent = disambSent sentHandler dmb
in showDoc . fmap onSent . parseDoc
-- | Training configuration.
data TrainConf = TrainConf
{ tagsetT :: TP.Tagset
, tiersT :: [P.Tier]
, schemaConfT :: SchemaConf
, sgdArgsT :: SGD.SgdArgs }
-- | Train disamb model.
train
:: Foldable f
=> F.Doc f s w -- ^ Document format handler
-> TrainConf -- ^ Training configuration
-> FilePath -- ^ Training file
-> Maybe FilePath -- ^ Maybe eval file
-> IO Disamb -- ^ Resultant model
train format TrainConf{..} trainPath evalPath'Maybe = do
crf <- Tier.train
(length tiersT)
sgdArgsT
(schemed format schema split trainPath)
(schemed format schema split <$> evalPath'Maybe)
return $ Disamb tagsetT tiersT schemaConfT crf
where
schema = fromConf schemaConfT
split = P.split tiersT . TP.parseTag tagsetT
-- | Schematized data from the plain file.
schemed
:: (Foldable f, Ord t)
=> F.Doc f s w -> Schema t a -> (F.Tag -> t)
-> FilePath -> IO [CRF.SentL Ob t]
schemed F.Doc{..} schema split path =
foldMap onSent . parseDoc <$> L.readFile path
where
F.Sent{..} = sentHandler
F.Word{..} = wordHandler
onSent sent =
[zip (schematize schema xs) (map mkDist xs)]
where
xs = map (Mx.mapWord split . extract) (parseSent sent)
mkDist = CRF.mkDist . M.toList . Mx.unWMap . Mx.tagWMap