Machine Translation Model using Inductive Logic Programming
Rule based machine translation systems face different challenges in building the translation model in a form of transfer rules. Some of these problems require enormous human effort to state rules and their consistency. This is where different human linguists make different rules for the same sentence. A human linguist states rules to be understood by human rather than machines. The proposed translation model (from Arabic to English) tackles the mentioned problem of building translation model. This model employs Inductive Logic Programming (ILP) to learn the language model from a set of example pairs acquired from parallel corpora and represent the language model in a rule-based format that maps Arabic sentence pattern to English sentence pattern. By testing the model on a small set of data, it generated translation rules with logarithmic growing rate and with word error rate 11%
Machine translation Inductive logic programming rule based machine translation ezample based machine translation rule Induction Arabic to English
Ahmad Hossny Khaled Shaalan Aly Fahmy
Faculty of Computers and Information, Cairo University Cairo, Egypt Faculty of Informatics British University in Dubai Dubai, UAE
国际会议
大连
英文
1-8
2009-09-24（万方平台首次上网日期，不代表论文的发表时间）