Question answering (QA) systems permit the user to ask a question using natural language, and the system provides a concise and correct answer. QA systems can be implemented for different types of datasets, structured or unstructured. In this paper, some of the recent studies will be reviewed and the limitations will be discussed. Consequently, the current issues are analyzed with the proposed solutions.

Solving winner determination problem in multiunit double auction has become an important E-business task. The main issue in double auction is to improve the reward in order to match the ideal prices and quantity and make the best profit for sellers and buyers according to their bids and predefined quantities. There are many algorithms introduced for solving winner in multiunit double auction. Conventional algorithms can find the optimal solution but they take a long time, particularly when they are applied to large dataset. Nowadays, some evolutionary algorithms, such as particle swarm optimization and genetic algorithm, were proposed and have been applied. In order to improve the speed of evolutionary algorithms convergence, we will propose a new kind of hybrid evolutionary algorithm that combines genetic algorithm (GA) with particle swarm optimization (PSO) to solve winner determination problem in multiunit double auction; we will refer to this algorithm as AUC-GAPSO.

Despite the accessibility of numerous online corpora, students and researchers engaged in the fields of Natural Language Processing (NLP), corpus linguistics, and language learning and teaching may encounter situations in which they need to develop their own corpora. Several commercial and free standalone corpora processing systems are available to process such corpora. In this study, we first propose a framework for the evaluation of standalone corpora processing systems and then use it to evaluate seven freely available systems. The proposed framework considers the usability, functionality, and performance of the evaluated systems while taking into consideration their suitability for Arabic corpora. While the results show that most of the evaluated systems exhibited comparable usability scores, the scores for functionality and performance were substantially different with respect to support for the Arabic language and N-grams profile generation. The results of our evaluation will help potential users of the evaluated systems to choose the system that best meets their needs. More importantly, the results will help the developers of the evaluated systems to enhance their systems and developers of new corpora processing systems by providing them with a reference framework.