# The Saudi Novel Corpus: Design and Compilation
## 2 Background and Related Works
### Arabic Corpora
Although the Arabic literature genre comprises part of the data of some general corpora, it is not clear what subgenres of Arabic literature are included in those corpora, i.e., we do not know whether Arabic or Saudi novels are included. An example of this issue is seen in the University of Jordon Arabic Corpus [46]. Although the developers of this corpus explicitly state that Arabic literature constitutes only 7% of the total of the corpus (around half a million words), no information is provided about the subgenres of Arabic literature. This drawback is also true for the NEMLAR Written Corpus [44], Corpus of Historical Arabic [34], and Corpus Linguistic Tools for Historical Semantics in Arabic [47]. 3. Some corpora use texts' publication location as a principal criterion to regionalize the text. In other words, the texts are attributed to the country in which the publisher is located, not to that of the writer's nationality. For example, when a particular novel is authored by a Saudi writer but published by a company situated outside Saudi Arabia (e.g., Lebanon), the text is ascribed to that country rather than to Saudi Arabia. Such an application is problematic since it results in some of the data produced by Saudi novelists appearing erroneously as non-Saudi data to users. A clear example of this practice is found in the KACST corpus. The author [36] explicitly states that 'text location' is one of the main criteria for categorizing the texts. This criterion indicateswhere the text was published, while the writer's nationality is entirely ignored. Hence, this corpus is not a reliable resource for a corpus-linguistic and stylistic study on Saudi novels. The same is true for the ICA [48]. 4. A few corpora specify the names of authors whose novels are included. As a result, writers' nationalities are easily identifiable. However, a careful examination of the metadata of the novels reveals that the dataset representing novels by Saudi authors is rather small. For instance, ArabiCorpus, which contains 32 Arabic novels with 1,026,171 words, explicitly provides the title and author's name for each Arabic novel included in the corpus; hence, we can easily determine their regional distributions. However, a closer look at authors' names identifies that only one Saudi novel is considered, whereas most of the novels are written by Egyptian novelists. Therefore, this corpus is too limited to examine Saudi novels. 5. Our survey has identified two corpora of data derived from Saudi novels. However, the developers of both corpora targeted only web novels written in different Saudi dialects. The first corpus, _Gumar_, is a corpus of Gulf dialects [49]. Saudi novels comprise around 60% of the total data included in this corpus. The second corpus, _Rewayatech_: Saudi Web Novels Dataset [50], was created to represent novels written using Saudi dialects. The data source for both corpora is online forums. Hence, the included novels are not edited, printed, nor published by a company. In addition, the authors of the novels included in the two corpora remain anonymous. This kind of data is entirely different from the data we intend to collect and analyze. Our corpus targets data from printed novels written by known authors using mainly Modern Standard Arabic (MSA). Therefore, web novels that are written using different Saudi dialects do not form a source for our corpus because they are not normally considered by scholars of Arabic literature as legitimate and do not represent an elevated use of Arabic. Consequently, they are typically neglected in linguistic and stylistic studies, i.e., researchers and critics of Saudi literature do not typically target those novels. Therefore, we decided to focus on the novels mainly written using MSA and published in print. Furthermore, a technical reason for avoiding novels written using Saudi dialects is that most NLP Arabic tools, including Part of Speech (POS) taggers, have been modeled to perform perfectly with MSA [51].