Machine translation with Javanese speech levels’ classification

A hybrid corpus-based machine processing has been developed to produce a proper Javanese speech level translation. The developed statistical memory-based machine translation shows significantly accurate results. Integration of an automatic text classifier and an expert system is proposed to help Javanese in classifying the speech levels used for a specific interlocutor. Javanese rule-based expert system is designed while naive Bayes classifier is selected after outperforming simple logic probability approach. As a result, the average of translation accuracy (72.3%) indicates that the integrated intelligent interfaces could effectively solve the Javanese language pragmatic translation problems.

[1]  Feifan Liu,et al.  Two-phase base noun phrase alignment in Chinese-English parallel corpora , 2005, 2005 International Conference on Natural Language Processing and Knowledge Engineering.

[2]  Magnus Merkel,et al.  A Simple Hybrid Aligner for Generating Lexical Correspondences in Parallel Texts , 1998, ACL.

[3]  Xuejiao Liu,et al.  Intrusion diagnosis and prediction with expert system , 2011, Secur. Commun. Networks.

[4]  Andrew Nafalski,et al.  Edit Distance Algorithm to Increase Storage Efficiency of Javanese Corpora , 2012 .

[5]  John-Christ Panayiotopoulos,et al.  Expert system personalized knowledge retrieval , 2011, Oper. Res..

[6]  Yibo Zhang,et al.  PECAT: a computer-aided translation tool based on bilingual corpora , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[7]  George Quinn,et al.  Teaching Javanese Respect Usage to Foreign Learners , 2011 .

[8]  Heshaam Faili,et al.  Generating english-persian parallel corpus using an automatic anchor finding sentence aligner , 2010, Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010).

[9]  Philip Koehn,et al.  Statistical Machine Translation , 2010, EAMT.

[10]  ANDY WAY,et al.  Comparing example-based and statistical machine translation , 2005, Nat. Lang. Eng..

[11]  V.M.D. Bilbao,et al.  Measuring the impact of cognates in parallel text alignment , 2005, 2005 portuguese conference on artificial intelligence.

[12]  Huang Heyan,et al.  Intelligent Case Based Machine Translation System , 2001 .

[13]  T. Van Hecke,et al.  Fuzzy expert system to characterize students. , 2011 .

[14]  Hiroshi Echizen-ya,et al.  Learning Method for Extraction of Partial Correspondence from Parallel Corpus , 2009, 2009 International Conference on Asian Language Processing.

[15]  Nancy J. Smith-Hefner,et al.  Language Shift, Gender, and Ideologies of Modernity in Central Java, Indonesia , 2009 .

[16]  Li Liu,et al.  Constructing a nutrition diagnosis expert system , 2012, Expert Syst. Appl..

[17]  Bo Wang,et al.  The automatic extraction of translation patterns and matching algorithm in an English-Chinese machine translation system , 2005, 2005 International Conference on Natural Language Processing and Knowledge Engineering.

[18]  Andrew Nafalski,et al.  Hybrid machine translation for Javanese speech levels , 2013, 2013 5th International Conference on Knowledge and Smart Technology (KST).

[19]  Miles Osborne,et al.  Statistical Machine Translation , 2010, Encyclopedia of Machine Learning and Data Mining.

[20]  John Hutchins Example-based machine translation: a review and commentary , 2006, Machine Translation.

[21]  John A. Kilpatrick,et al.  Expert systems and mass appraisal , 2011 .

[22]  Harold L. Somers,et al.  Review Article: Example-based Machine Translation , 1999, Machine Translation.

[23]  Juan Luis Castro,et al.  A fuzzy expert system for business management , 2010, Expert Syst. Appl..

[24]  Gloria Poedjosoedarmo,et al.  The effect of Bahasa Indonesia as a lingua franca on the Javanese system of speech levels and their functions , 2006 .

[25]  Kentaro Ogura,et al.  Reference in Japanese–English Machine Translation , 1998, Machine Translation.