Rule Induction
Shaalan, K., and A. H. Hossny,
"Automatic rule induction in Arabic to English machine translation framework",
Challenges for Arabic Machine Translation, Amsterdam, The Netherlands, John Benjamins Publishing Company, 2012.
Abstract This paper addresses exploiting a supervised machine learning technique to automatically induce Arabic-to-English transfer rules from chunks of parallel aligned linguistic resources. The induced structural transfer rules encode the linguistic translation knowledge for converting an Arabic syntactic structure into a target English syntactic structure. These rules are going to be an integral part of an Arabic-English transfer-based machine translation. Nevertheless, a novel morphological rule induction method is employed for learning Arabic morphological rules that are applied in our Arabic morphological analyzer. To demonstrate the capability of the automated rule induction technique we conducted rule-based translation experiments that use induced rules from a relatively small data set. The translation quality of the hybrid translation experiments achieved good results in terms of WER.
Hossny, A., K. Shaalan, and A. Fahmy,
"Automatic Morphological Rule Induction for Arabic",
The sixth international conference on Language Resources and Evaluation (LREC'08) workshop on HLT & NLP within the Arabic world: Arabic Language and local languages processing: Status Updates and Prospects, Marrakech, Morocco, LREC, pp. 97–101, may, 2008.
AbstractIn this paper, we introduce an algorithm for morphological rule induction using meta-rules for Arabic morphology based on inductive logic programming. The processing resources are a set of example pairs (stem and inflected form) with their feature vectors, either positive or negative, and the linguistic background knowledge from the Arabic morphological analysis domain. Each example pair has two words to be analyzed vocally into consonants and vowels. The algorithm applies two levels of mapping: between the vocal representation of the two words (stem, morphed) and between their feature vector. It differentiates between both mappings in order to accurately deduce which changes in the word structure led to which changes in its features. The paper also addresses the irregularity, productivity and model consistency issues. We have developed an Arabic morphological rule induction system (AMRIS). Successful evaluation has been performed and showed that the system performance results achieved were satisfactory.
