úÎ )á'®        The , type is the main type of WeightedSemiring. 9 It combines scoring semiring with a history semiring. <The best example of this is the ViterbiDerivation semiring. A 2 also includes a sensical ordering over choices. S i.e. out of two choices which is better. This is used for Viterbi selection. A 8 is made up of an additive Monoid and a Multiplicative. S It must also satisfy several other algebraic properties checked by quickcheck. !"#$%&'()The = semiring keeps track of the likelihood of the known output 6 by keeping track of the probability of all paths.  The   < semiring keeps track of the n best scoring path to a known 6 output. This score is determined by a user defined . UThe value of n (the number of of values to rank) is included in the type to prevent Z combining mismatching values. To create a new n, make a new unary type and an instance  of N.   data Ten = Ten  instance N Ten where  mkN = Ten  n _ = 10       *The . semiring keeps track of the number of paths ) or derivations led to a given output. The 5 semiring keeps track of a all paths or derivations J that led to the known output. This can be useful for debugging output. 1Keeping all these paths around can be expensive.  leaves open T the implementation of the internal path monoid for more compact representations. The 6 semiring keeps track of a single path or derivation N that led to the known output. If there are more than one path it discards O in favor the lesser path (based on ord). The main purpose of this semiring W is to track derivations for ViterbiNBestDerivation. If you want to keep all paths,  use . RDerivation takes a Monoid as an argument that describes how to build up paths or " more complicated structures. The 9 is a simpler semiring. It just keeps track of the best  scoring path and it's derivation. D type ViterbiDerivation m = Viterbi (Weighted Prob (Derivation m)) The . is an example of a more complicated semiring P built up from smaller components. It keeps track of the top N scoring paths ! along with their derivations. Q type ViterbiNBestDerivation n m = ViterbiNBest n (Weighted Prob (Derivation m)) +       !"#$"#%"#&"#'()*()+(),-./0 semiring-0.2 NLP.SemiringNLP.Semiring.BooleanNLP.Semiring.ProbNLP.Semiring.ViterbiNBestNLP.Semiring.ViterbiNLP.Semiring.CountingNLP.Semiring.Derivation#NLP.Semiring.ViterbiNBestDerivationNLP.Semiring.HelpersWeightedWeightedSemiringSemiringBooleanProb Viterbi10BestTen ViterbiNBestNmkNnViterbiOne fromViterbiCountingMultiDerivation Derivation mkDerivationfromDerivationViterbiDerivationViterbiNBestDerivationgetBestDerivation getBestScore cartesianbase Data.MonoidmemptymconcatmappendMonoidmonoids-0.2.0.2Data.Monoid.MultiplicativetimesoneMultiplicative getWeightgetInfo mkViterbi