!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~  Safe-Inferred9An atomic part of morphosyntactic tag with optional POS. A tier description. $Does it include the part of speech? Tier grammatical attributes. Select tier attributes. Split the positional tag.  None5A set with a non-negative weight assigned to each of  its elements. 2A sentence with original, textual representation.  A sentence. Orthographic form. Out-of-vocabulary (OOV) word. 8A segment parametrized over a word type and a tag type. 9A word represented by the segment. Typically it will be  an instance of the  class. 2A set of interpretations. To each interpretation 0 a weight of appropriateness within the context  is assigned.  Map function over segment tags.  Interpretations of the segment.  Interpretations of the segment. !Map function over sentence tags. !Map function over sentence tags. @Make a weighted collection. Negative elements will be ignored. 1Map function over weighted collection elements.        None None@Synchronize two datasets, taking disamb tags from the first one 2 and the rest of information form the second one. L In case of differences in token-level segmentation, reference segmentation G (token-level) is assumed. Otherwise, it would be difficult to choose  correct disamb tags. JIf both arguments contain only one segment, insert disamb interpretations L from the first segment into the second segment. Otherwise, the first list ) of segments will be returned unchanged. Align two lists of segments. DFind the shortest, length-matching prefixes in the two input lists. NoneIAn analyser performs word-level segmentation and morphological analysis. Reanalyse sentence. 0From the reference sentence the function takes:  Word-level segmentation  Chosen interpretations (tags) 1From the reanalysed sentence the function takes:  Potential interpretations Reanalyse paragraph. None$ EConfiguration of the schema. All configuration elements specify the L range over which a particular observation type should be taken on account.  For example, the  [-1, 0, 2]- range means that observations of particular 2 type will be extracted with respect to previous (k - 1 ), current (k)  and after the next (k + 2-) positions when identifying the observation  set for position k in the input sentence. "The 7 schema block. #The 8 schema block. $The 9' schema block. The first list of ints ! represents lengths of prefixes. %The :' schema block. The first list of ints ! represents lengths of suffixes. &The ; schema block. 'The < schema block. (The = schema block. )The > schema block. * Maybe entry. +Body of configuration entry. -&Range argument for the schema block. .1When true, the entry is used only for oov words. /+Additional arguments for the schema block. 0>A block is a chunk of the Ox computation performed within the F context of the sentence and the list of absolute sentence positions. 1Record structure of the basic observation types. 1?A schema is a block of the Ox computation performed within the = context of the sentence and the absolute sentence position. 2CThe Ox monad specialized to word token type and text observations. 3@An observation consist of an index (of list type) and an actual  observation value. 4A dummy schema block. 5HSequence the list of schemas (or blocks) and discard individual values. Construct the  structure given the sentence. 6+Transform a block to a schema depending on * * A list of relative sentence positions, 3 * A boolean value; if true, the block computation 4 will be performed only on positions where an OOV  word resides. 7+Orthographic form at the current position. 8+Orthographic form at the current position. 9.List of lowercased prefixes of given lengths. :.List of lowercased suffixes of given lengths. ;Shape of the word. <Shape of the word. =Packed shape of the word. >Packed shape of the word. ?!Entry with additional arguemnts. @*Plain entry with no additional arugments. A.Null configuration of the observation schema. B-Build the schema based on the configuration. C:Use the schema to extract observations from the sentence. - !"#$%&'()*+,-./0123456789:;<=>?@ABC$ !"#$%&'()*+,-./0123456789:;<=>?@ABC$32145C+,-./*@? !"#$%&'()AB06789:;<=> !"#$%&'()*+,-./0123456789:;<=>?@ABCNoneDTraining configuration. GSGD parameters. HStore SGD dataset on disk IR0 construction method J7Method of constructing the default set of labels (R0). KSee  LSee  MSee  NA guessing model. 0Schematize the input sentence with according to schema rules. RDetermine the k5 most probable labels for each word in the sentence. ? TODO: Perhaps it would be better to use sets instead of lists  as output? SAInsert guessing results into the sentence. Only interpretations  of OOV words will be extended. TCombine R with S. UTrain guesser. Schematized dataset. DEFGHIJKLMNOPQRSTUTraining configuration Training data Evaluation data DEFGHIJKLMNOPQRSTUNOPQRSTDEFGHIJMLKU DEFGHIJMLKNOPQRSTUNone VTraining configuration. ^A disambiguation model. 0Schematize the input sentence with according to schema rules. 2Unsplit the complex tag (assuming, that it is one & of the interpretations of the word). c*Perform context-sensitive disambiguation. d1Insert disambiguation results into the sentence. eCombine c with d. f(Tag labels with marginal probabilities. g@Prune disamb model: discard model features with absolute values 1 (in log-domain) lower than the given threshold. hTrain disamb model. &Schematized data from the plain file. VWXYZ[\]^_`abcdefghTraining configuration Training data Evaluation data Resultant model VWXYZ[\]^_`abcdefgh^_`abfcdeVYWZ[\]X\]hg V YWZ[\]X\]^_`abcdefghNone i Statistics. j"Number of segments in gold corpus kNumber of correct tags  Add stats, mAccuracy given stats. nAccuracy weak lower bound. oAccuracy strong lower bound. pAccuracy weak upper bound. qAccuracy strong upper bound. All tags are expanded here. Positive tags. ijklmnopq ijklmnopq ijklmnpoq ijklmnopqNone rConcraft data. xASave model in a file. Data is compressed using the gzip format. yLoad model from a file. z@Tag sentence using the model. In your code you should probably C use your analysis function, translate results into a container of  ences, evaluate z on each sentence and embed the A tagging results into the morphosyntactic structure of your own. )The function returns guessing results as  elements 3 of the output pairs and disambiguation results as  & elements of the corresponding pairs. {=Determine marginal probabilities corresponding to individual A tags w.r.t. the disambiguation model. Since the guessing model A is used first, the resulting weighted maps corresponding to OOV ; words may contain tags not present in the input sentence. | Train the r! model after dataset reanalysis. The  and ' instances are used to store processed * input data in temporary files on a disk. } Train the r model. 4 No reanalysis of the input data will be performed. The  and ' instances are used to store processed * input data in temporary files on a disk. ~8Prune disambiguation model: discard model features with A absolute values (in log-domain) lower than the given threshold. BStore dataset on a disk and run a handler on a list which is read ? lazily from the disk. A temporary file will be automatically $ deleted after the handler is done. rstuvwxyz{|"A morphosyntactic tagset to which s - of the training and evaluation input data  must correspond. 'Analysis function. It will be used to  reanalyse input dataset. -How many tags is the guessing model supposed 1 to produce for a given OOV word? It will be  used (see T) on both training and / evaluation input data prior to the training  of the disambiguation model. /Training configuration for the guessing model. Training configuration for the  disambiguation model. *Training dataset. This IO action will be 1 executed a couple of times, so consider using $ lazy IO if your dataset is big. .Evaluation dataset IO action. Consider using # lazy IO if your dataset is big. }"A morphosyntactic tagset to which s - of the training and evaluation input data  must correspond. -How many tags is the guessing model supposed 1 to produce for a given OOV word? It will be  used (see T) on both training and / evaluation input data prior to the training  of the disambiguation model. /Training configuration for the guessing model. Training configuration for the  disambiguation model. *Training dataset. This IO action will be 1 executed a couple of times, so consider using $ lazy IO if your dataset is big. .Evaluation dataset IO action. Consider using # lazy IO if your dataset is big. ~ Directory to create the file in  Template for  Input dataset Handler rstuvwxyz{|}~ rstuvwxyz{}|~ rstuvwxyz{|}~     !"#$%&''()*+,-./01123456789:;<=>?@ABCDEFGHIIJKLMNOPQRRSTUVWXIYZI[JKL\\]ST^V_`aXbbcdefghijjklm^nop`qXarstuvwx y z { | } ~  HHconcraft-0.9.3NLP.Concraft.DisambNLP.Concraft.MorphosyntaxNLP.Concraft.AnalysisNLP.Concraft.SchemaNLP.Concraft.Guess"NLP.Concraft.Morphosyntax.Accuracy NLP.ConcraftNLP.Concraft.Disamb.PositionalNLP.Concraft.Format.TempNLP.Concraft.Morphosyntax.AlignAtomposattsTierwithPoswithAttsWMapunWMapSentOsegsorigSentWordorthoovSegwordtagsmapSeg interpsSetinterpsmapSentmapSentOmkWMapmapWMapAnalyse reAnaSentreAnaPar SchemaConforthClowOrthC lowPrefixesC lowSuffixesCknownCshapeCpackedC begPackedCEntryBodyrangeoovOnlyargsBlockSchemaOxObvoid sequenceS_ fromBlockorthBlowOrthB lowPrefixesB lowSuffixesBknownBshapeBpackedB begPackedB entryWithentrynullConffromConf schematize TrainConf schemaConfTsgdArgsTonDiskTr0TR0T OovChosen AnyChosen AnyInterpsGuesser schemaConfcrfguessinclude guessSenttrain ReTrainConfinitDmbtiersTDisambtiersdisamb disambSent marginalspruneStatsgoodgoldaccuracyweakLBstrongLBweakUBstrongUBConcrafttagsetguessNumguesser saveModel loadModeltag reAnaTrainselectsplit $fBinaryAtom $fBinaryTier $fWordSeg $fFromJSONSeg $fToJSONSeg encodePar decodeParwriteParreadParsync moveDisambalignmatchBaseObmkBaseOblowOrthmkArg0mkArg1$fBinarySchemaConf $fBinaryBodycrf-chain1-constrained-0.3.1!Data.CRF.Chain1.Constrained.Train oovChosen anyChosen anyInterpsschemed$fBinaryGuesserunSplit$fBinaryDisamb.+.choicepositivebase Data.Tuplefstsnd aeson-0.7.0.6Data.Aeson.Types.ClassFromJSONToJSONwithTemp modelVersiontagset-positional-0.3.0Data.Tagset.PositionalTagtemporary-1.1.2.5System.IO.Temp withTempFile$fBinaryConcraft