Safe Haskell	None

Data.CRF.Chain2.Pair

Contents

Data types
- External
Internal
CRF
- Training
- Tagging
Feature selection

Synopsis

Data types

External

data Word a b Source

A word consists of a set of observations and a set of potential labels.

Instances

(Eq a, Eq b) => Eq (Word a b)
(Eq (Word a b), Ord a, Ord b) => Ord (Word a b)
(Show a, Show b) => Show (Word a b)

mkWord :: Set a -> Set b -> Word a bSource

A word constructor which checks non-emptiness of the potential set of labels.

type Sent a b = [Word a b]Source

A sentence of words.

data Dist a Source

A probability distribution defined over elements of type a. All elements not included in the map have probability equal to 0.

mkDist :: Ord a => [(a, Double)] -> Dist aSource

Construct the probability distribution.

type WordL a b = (Word a b, Dist b)Source

A WordL is a labeled word, i.e. a word with probability distribution defined over labels. We assume that every label from the distribution domain is a member of the set of potential labels corresponding to the word. TODO: Ensure the assumption using the smart constructor.

type SentL a b = [WordL a b]Source

A sentence of labeled words.

Internal

newtype Ob Source

Constructors

Ob
Fields unOb :: Int

Instances

Eq Ob
Ord Ob
Show Ob
Ix Ob
Binary Ob

newtype Lb1 Source

Constructors

Lb1
Fields unLb1 :: Int

Instances

Eq Lb1
Ord Lb1
Show Lb1
Ix Lb1
Binary Lb1

newtype Lb2 Source

Constructors

Lb2
Fields unLb2 :: Int

Instances

Eq Lb2
Ord Lb2
Show Lb2
Ix Lb2
Binary Lb2

type Lb = (Lb1, Lb2)Source

data Feat Source

Constructors

TFeat3'1 !Lb1 !Lb1 !Lb1
TFeat3'2 !Lb2 !Lb2 !Lb2
TFeat2'1 !Lb1 !Lb1
TFeat2'2 !Lb2 !Lb2
TFeat1'1 !Lb1
TFeat1'2 !Lb2
OFeat'1 !Ob !Lb1
OFeat'2 !Ob !Lb2

Instances

Eq Feat
Ord Feat
Show Feat
Binary Feat
FeatMap FeatMap Feat
Binary (FeatMap Feat)

CRF

data CRF a b c Source

Constructors

CRF
Fields codecData :: CodecData a b c model :: Model FeatMap Ob Lb Feat

Instances

(Ord a, Ord b, Ord c, Binary a, Binary b, Binary c) => Binary (CRF a b c)

Training

train Source

Arguments

:: (Ord a, Ord b, Ord c)
=> SgdArgs	Args for SGD
-> FeatSel Ob Lb Feat	Feature selection
-> IO [SentL a (b, c)]	Training data `IO` action
-> Maybe (IO [SentL a (b, c)])	Maybe evalation data
-> IO (CRF a b c)	Resulting codec and model

Train the CRF using the stochastic gradient descent method. When the evaluation data IO action is Just, the iterative training process will notify the user about the current accuracy on the evaluation part every full iteration over the training part. Use the provided feature selection function to determine model features.

Tagging

tag :: (Ord a, Ord b, Ord c) => CRF a b c -> Sent a (b, c) -> [(b, c)]Source

Find the most probable label sequence.

Feature selection

type FeatSel o t f = FeatGen o t f -> Xs o t -> Ys t -> [f]Source

A feature selection function type.

selectHidden :: FeatSel o t fSource

The hiddenFeats adapted to fit feature selection specs.

selectPresent :: FeatSel o t fSource

The presentFeats adapted to fit feature selection specs.