Within the Givenness Hierarchy framework of Gundel, Hedberg, & Zacharski (1993), lexical items included in referring forms are assumed to conventionally encode two kinds of information: conceptual information about the speaker’s intended referent and procedural information about the assumed cognitive status of that referent in the mind of the addressee, which is encoded by various determiners and pronouns. In this paper, we focus on effects of underspecification of cognitive status, establishing that, while salience and degree of accessibility play an important role in reference production and understanding, the Givenness Hierarchy itself is not a hierarchy of degrees of salience/accessibility, contrary to what has often been assumed. The framework is thus able to account for a number of interesting experimental results in the literature without making additional assumptions about form-specific constraints

Zacharski, Ron; Ahmed Abdelali; Stephen Helmreich; and Jim Cowie. 2009. Linguistic Dumpster Diving: Geographical Classification of Arabic Text. Proceedings of the Chicago Colloquia on Digital Humanities and Computer Science. (pdf)

Abdelali, Ahmed, Steve Helmreich, and Ron Zacharski. 2009. Investigations on Standard Arabic Geographical Classification. Proceedings of the Computational Approaches to Arabic Script-based Languages Workshop, Ottawa, 26 August 2009. (pdf)

Regarding installing Snow Leopard, I was unsuccessful in getting either the boot CD method or the USB method to work with this build (both methods described on the  tonymacx86 website). The method that did work was to install the OS on the hard drive by using another Mac. My Hackintosh runs the stock 10.6.2 kernel. I used a DSDT specific to my motherboard and available at DSDT Database for P55 Motherboards. To get networking working I used a kext specific to the network chipset. Most of the information on how to do this is available on the tonymacx86 website. Sound does not work even with trying various kexts specific to the audio chipset. I hope to resolve this by using a usb audio interface. Other than sound, everything seems to be working great!

Sudan المقاولون الساحل برانكو اعداده الموردة استعدادا
Egypt  المقاولون الساحل برانكو الموردة التضامن اعداده
UK المقاولون الساحل برانكو اعداده التضامن الامل
Libya  مدني الساحل استعدادا الامل الاولمبي حليم
Syria بشار البوكمال المادة النادي الفنان الشاعر

Our work focused on answering two questions: (1) can we geographically classify documents solely on the frequency of common words, and, (2) rather than dialects, can we classify regional variations in one dialect (for example, can we classify regional differences in Modern Standard Arabic). We developed a series of studies aimed at answering these questions. These studies showed that it is possible to accurately classify newspaper documents solely using the common words in the documents. One study compared the performance of 10 classifiers on this task and provided some evidence that Bagging C4.5, C4.5, and SMO with a polynomial kernel produce the most accurate classifiers. One major limitation of these studies is that they relied on a single data source for each country. Because a single newspaper source was used for each region, it could be argued that the classifiers were classifying the documents based on the newspaper rather than on geographical region. To examine this possibility, we evaluated the performance of the classifier on a different genre: forum posts. The results here are less than compelling; nevertheless the classifier had moderate accuracy on classifying forum posts.1 We will examine this in more detail in future work using a larger corpus from a wider breadth of sources. Finally, we examined the effect of document size on classification accuracy finding that we could get good classification accuracy even for relatively short documents. These studies suggest that the answer to both questions raised in the beginning sentence of this paragraph is yes: yes we can geographically classify document based on common word frequency and yes we can classify regional differences in Modern Standard Arabic.
This work has direct practical application to intelligence tasks. It may help in determining the author of an anonymous document. For example, a geographical classifier can be used as one module of a system designed to detect cyber terrorist threats against the U.S. by aiding in the identification of the source of the threat. Finally, many Arabic scholars (Shukri B. Abed, p.c.) believe there are no regional variations of Modern Standard Arabic. The work reported on here provides some support for the alternative view that there are regional variations (see, for example, Ibrahim and Ibrahim, 2009 and Abdelali, 2004). Future work using larger corpora from a broad number of sources may provide stronger evidence for this position. (PDF of the draft paper)