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In this paper, we propose a method to disambiguate the output of a morphological analyzer of the Tunisian dialect. We test three machine-learning techniques that classify the morphological analysis of each word token into two classes: true and false. The class label is assigned to each analysis according to the context of the corresponding word in a sentence. In failure cases, we combine the results of the proposed techniques with a bigram classifier to choose only one analysis for a given word. We disambiguate the result of the morphological analyzer of the Tunisian Dialect Al-Khalil-TUN (Zribi et al., 2013b). We use the Spoken Tunisian Arabic Corpus STAC (Zribi et al., 2015) to train and test our method. The evaluation shows that the proposed method has achieved an accuracy performance of 87.32%. (C) 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Market risk management is a critical, resource-intensive task for financial trading firms. The industry relies heavily on innovation in technical infrastructure to increase the quality and quantity of risk management information and to reduce the cost of its production. However, until recently, the industry has lacked an independent standard for gauging the potential of new technologies to help. This changed when the STAC Benchmark (TM) Council developed STAC-A2 (TM), a vendor-independent benchmark suite based on real-world market risk analysis workloads. It was specified by trading firms and made actionable by leading HPC vendors. Unlike vendor-developed benchmarks known to the authors, STAC-A2 satisfies all of the requirements important to end-user firms: relevance, neutrality, scalability, and completeness. Intel has demonstrated the utility of STAC-A2 for comparing successive generations of Intel (R) Xeon (R) processors.