Mathematical Method of Translation into Ukrainian Sign Language Based on Ontologies

This paper introduces the mathematical method for translation into sign language based on ontologies. The modification of affix context-free grammar (AGFL) that adds semantical attribute and a new form of production called the “template production” is discussed. This new form helps to represent ontology-based productions in a short and computationally inexpensive way. The mathematical method utilizes dictionaries, ontology database, weighted affix context-free grammar (WACFG) parser, algorithm for transformation of constituency tree into dependency tree, and an algorithm for synthesis of Ukrainian sign language glosses. The algorithm for selection and convertion of grammatically augmented ontology (GAO) expressions into the set of WACFG productions is suggested. The major increase in percentage of correctly parsed sentences was achieved for Ukrainian sign language (UKL) and Ukrainian spoken language (USpL). All algorithms are components of the translation system for Ukrainian sign language. Simple video sequencing is utilized for sign language synthesis, however any other sign animation tool can be used. Tasks that require further research are defined.

[1]  Barbara Plank,et al.  Subdomain Sensitive Statistical Parsing using Raw Corpora , 2008, LREC.

[2]  O. Lozynska,et al.  Spoken and sign language processing using grammatically augmented ontology , 2015 .

[3]  Philipp Cimiano,et al.  Generating LTAG grammars from a lexicon/ontology interface , 2010, TAG.

[4]  Ilyas Cicekli,et al.  An Ontology-Based Approach to Parsing Turkish Sentences , 1998, AMTA.

[5]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[6]  Oleksandr Marchenko,et al.  Determining Semantic Valences of Ontology Concepts by Means of Nonnegative Factorization of Tensors of Large Text Corpora , 2014 .

[7]  Volodymyr Pasichnyk,et al.  Partial semantic parsing of sentences by means of grammatically augmented ontology and weighted affix context-free grammar , 2017 .

[8]  Noam Chomsky,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[9]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[10]  R. Durbin,et al.  RNA sequence analysis using covariance models. , 1994, Nucleic acids research.

[11]  Sang Keun Rhee,et al.  Ontology-based Semantic Relevance Measure , 2007, SWW 2.0.

[12]  Eduard H. Hovy,et al.  Building Japanese-English Dictionary based on Ontology for Machine Translation , 1994, HLT.

[13]  Amit P. Sheth,et al.  Altering document term vectors for classification: ontologies as expectations of co-occurrence , 2007, WWW '07.

[14]  Francis Jeffry Pelletier,et al.  Representation and Inference for Natural Language: A First Course in Computational Semantics , 2005, Computational Linguistics.

[15]  Alessandro Mazzei,et al.  An Ontology Based Architecture for Translation , 2011, IWCS.

[16]  O. Lozynska,et al.  Information technology for Ukrainian Sign Language translation based on ontologies , 2015 .