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This module defines the interface for AlignmentAlgorithms as well as some helper classes. An AlignmentAlgorithm computes an Alignment of two given input sequences, given a Comparator that works in these sequences. More details on the AlignmentAlgorithm can be found in the respective interface. More information on Comparators can be found in the comparators module. The resulting 'Alignment' may be just a real-valued dissimilarity between the input sequence or may incorporate additional information, such as a full Alignment, a PathList, a PathMap or a CooptimalModel. If those results support the calculation of a Gradient, they implement the DerivableAlignmentDistance interface. In more detail, the Alignment class represents the result of a backtracing scheme, listing all Operations that have been applied in one co-optimal Alignment. A classic AlignmentAlgorithm does not result in a differentiable dissimilarity, because the minimum function is not differentiable. Therefore, this package also contains utility functions for a soft approximation of the minimum function, namely Softmin. For faster (parallel) computation of many different alignments or gradients we also provide the ParallelProcessingEngine, the SquareParallelProcessingEngine and the ParallelGradientEngine.

This module defines the interfaces for Comparators in the TCS Alignment Toolbox. A Comparator has the purpose of defining the dissimilarity between elements in the input sequences of an Alignment. More specific information on Comparators can be found in the 'Comparator' interface. You can find a lot of helpful standard implementations of Comparators in the comparators-lib module. In the TCS Alignment Toolbox we require the output values of Comparators to lie in the range [0,1]. Many natural dissimilarities on value sets do not meet this criterion, such that additional normalization has to be applied. To that end this package also contains a Normalizer interface for functions that map real values from the range [0, infinity) to the range [0,1]. This package also provides a few convenience implementations of the Comparator interface to make the implementation of custom Comparators simpler, namely: SkipExtendedComparator, ParameterLessSkipExtendedComparator, ComparisonBasedSkipExtendedComparator, and ParameterLessComparisonBasedSkipExtendedComparator. Finally the TCS Alignment Toolbox also provides the means to learn parameters of Comparators. To enable that Comparators must implement the DerivableComparator interface to properly define the parameters that can be learned and the gradient of the dissimilarity with respect to these parameters. Gradients are stored using the Gradient interface as well as some convenience implementations of said interface, namely EmptyGradient, SingletonGradient, ArrayGradient and ListGradient.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616 This version allows defining section scopes for sentence segmentation.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616