crf-chain1-constrained-0.6.0: First-order, constrained, linear-chain conditional random fields

Data.CRF.Chain1.Constrained.Train

Contents

Synopsis

# Model

data CRF a b Source #

A conditional random field model with additional codec used for data encoding.

Constructors

 CRF Fieldscodec :: Codec a bThe codec is used to transform data into internal representation, where each observation and each label is represented by a unique integer number.model :: ModelThe actual model, which is a map from Features to potentials.
Instances
 (Ord a, Ord b, Binary a, Binary b) => Binary (CRF a b) Source # Instance detailsDefined in Data.CRF.Chain1.Constrained.Train Methodsput :: CRF a b -> Put #get :: Get (CRF a b) #putList :: [CRF a b] -> Put #

# Training

Arguments

 :: (Ord a, Ord b) => SgdArgs Args for SGD -> Bool Store dataset on a disk -> ([SentL a b] -> Set b) R0 construction -> (AVec Lb -> [(Xs, Ys)] -> [Feature]) Feature selection -> IO [SentL a b] Training data IO action -> IO [SentL a b] Evaluation data -> IO (CRF a b) Resulting model

Train the CRF using the stochastic gradient descent method.

The resulting model will contain features extracted with the user supplied extraction function. You can use the functions provided by the Data.CRF.Chain1.Constrained.Feature.Present and Data.CRF.Chain1.Constrained.Feature.Hidden modules for this purpose.

You also have to supply R0 construction method (e.g. oovChosen) which determines the contents of the default set of labels.

# R0 construction

oovChosen :: Ord b => [SentL a b] -> Set b Source #

Collect labels assigned to OOV words.

anyChosen :: Ord b => [SentL a b] -> Set b Source #

Collect labels assigned to words in a dataset.

anyInterps :: Ord b => [SentL a b] -> Set b Source #

Collect interpretations (also labels assigned) of words in a dataset.