The present work reports our attempt in developing an English-Arabic bi-directional Machine Translation (MT) tool in the agriculture domain. It aims to achieve automated translation of expert systems. In particular, we describe the translation of knowledge base, including, prompts, responses, explanation text, and advices. In the central laboratory for agricultural expert systems, this tool is found to be essential in developing bi-directional (English-Arabic) expert systems because both English and Arabic versions are needed for development, deployment, and usage purpose. The tool follows the rule-based transfer MT approach. A major design goal of this tool is that it can be used as a stand-alone tool and can be very well integrated with a general (English-Arabic) MT system for Arabic scientific text. The paper also discusses our experience with the developed MT system and reports on results of its application on real agricultural expert systems.

Arabic Person Name Recognition has been tackled mostly using either of two approaches: a rule-based or Machine Learning (ML) based approach, with their strengths and weaknesses. In this paper, the problem of Arabic Person Name Recognition is tackled through integrating the two approaches together in a pipelined process to create a hybrid system with the aim of enhancing the overall performance of Person Name Recognition tasks. Extensive experiments are conducted using three different ML classifiers to evaluate the overall performance of the hybrid system. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches. Moreover, our system outperforms the state-of-the-art of Arabic Person Name Recognition in terms of accuracy when applied to ANERcorp dataset, with precision 0.949, recall 0.942 and f-measure 0.945.

This research suggests a method for query expansion on Arabic Information Retrieval using Expectation Maximization (EM). We employ the EM algorithm in the process of selecting relevant terms for expanding the query and weeding out the non-related terms. We tested our algorithm on INFILE test collection of CLLEF2009, and the experiments show that query expansion that considers similarity of terms both improves precision and retrieves more relevant documents. The main finding of this research is that we can increase the recall while keeping the precision at the same level by this method.

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