scholarly journals Representation of Yine [Arawak] Morphology by Finite State Transducer Formalism

Author(s):  
Adriano Ingunza Torres ◽  
John Miller ◽  
Arturo Oncevay ◽  
Roberto Zariquiey Biondi
2007 ◽  
Vol 18 (04) ◽  
pp. 859-871
Author(s):  
MARTIN ŠIMŮNEK ◽  
BOŘIVOJ MELICHAR

A border of a string is a prefix of the string that is simultaneously its suffix. It is one of the basic stringology keystones used as a part of many algorithms in pattern matching, molecular biology, computer-assisted music analysis and others. The paper offers the automata-theoretical description of Iliopoulos's ALL_BORDERS algorithm. The algorithm finds all borders of a string with don't care symbols. We show that ALL_BORDERS algorithm is an implementation of a finite state transducer of specific form. We describe how such a transducer can be constructed and what should be the input string like. The described transducer finds a set of lengths of all borders. Last but not least, we define approximate borders and show how to find all approximate borders of a string when we concern Hamming distance definition. Our solution of this problem is based on transducers again. This allows us to use analogy with automata-based pattern matching methods. Finally we discuss conditions under which the same principle can be used for other distance measures.


2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeffrey Micher

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.


Author(s):  
Cyril Allauzen ◽  
Michael Riley ◽  
Johan Schalkwyk ◽  
Wojciech Skut ◽  
Mehryar Mohri

2010 ◽  
Author(s):  
Lluís-F. Hurtado ◽  
Joaquin Planells ◽  
Encarna Segarra ◽  
Emilio Sanchis ◽  
David Griol

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