# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 2 Background
### Arabic Language  
Arabic is a Semitic language [29]. It differs from Indo-European languages alphabetically, morphologically, and syntactically. From an alphabetical perspective, there are twenty-eight Arabic letters. They have contextual variants that can be presented in different shapes depending on their position in words. For example, the letter \"\(\zeta\)\" (in this paper, an Arabic word is represented in some or all of three variants according to context: \"Arabic word\" ([Buckwalter Arabic transliteration [30]]): English translation) can be represented as \"\(\_\)\" if the letter appears at the beginning of a word like \"\(\zeta\)\" ([Elm]: know), \"\(\_\)\" if it appears in the middle like \"\(\zeta\)\" ([AlElm]: the knowledge), \"\(\zeta\)\" if it is at the end like \"\(\zeta\)\" ([qTE]: cut), or \"\(\zeta\)\" if it appears independent of other letters like \"\(\zeta\)\" ([bAE]: sold). Moreover, Arabic does not support capitalization, which increases the difficulty of some information extraction and understanding tasks, including SCD.  
From a morphology perspective, the Arabic template morphology interweaves patterns and affixes to roots [31]. For instance, the word \"\(\zeta\)\" ([AlTIAb]: the students) was coined starting with the root: \"\(\zeta\)\" ([Tb]: request). Then the pattern \"\(\zeta\)\" ([fEAI]) is applied to produce \"\(\zeta\)\" ([TIAb]: students). Finally, the prefix \"\(\zeta\)\" ([AI]: the) precedes the pattern to get the final word \"\(\zeta\)\" \(\zeta\)\".\" This structure differs from the concatenative languages, such as the English word \"the students\", which was constructed by concatenating \"the\" and \"\(s\)\" with the word \"student\". In some cases, a single Arabic word represents an English sentence. For example, the Arabic word \"\(\zeta\)\"\(\zeta\)\" ([f\(\zeta\)sqynAkmwh]: then we give it to you to drink) can be translated into English using eight words.  
\begin{table}
\begin{tabular}{l|l|l} \hline \hline  & **Document 1** & **Document 2** \\ \hline \multirow{4}{*}{Author 1} & This paragraph was written & This paragraph was written \\  & by the first author. & by the first author. \\ \cline{2-3}  & This paragraph has two sentences. They were written & Two sentences are in this \\  & by the first author. & \\  & This paragraph as well was & The second author wrote \\  & written by the first author. & this paragraph too. \\  & This paragraph was also & This paragraph also was \\  & written by the first author. & written by the first author. \\ \hline Para. level & [0, 0, 0] & [1, 0, 1] \\ Sent. level & [0, 0, 0, 0] & [1, 0, 0, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of the SCD task.  
From a syntactic perspective, several Arabic grammatical rules differ from English ones. Arabic is a relatively free word-order language [29]. The order of sentences can be verb-subject-object, subject-verb-object, or object-verb-subject. Unlike English, Arabic sentences consider syntactic constituency. A noun and its modifiers must correspond in gender, number, and definiteness. In the case of two items, the quantifier and the noun must agree in gender. For example, \"_j_'_  
adopted a distribution test called the Wilcoxon Signed Rank Test [36] to predict the style changes. This test showed no change in style if two documents were derived from the same distribution; a change in the style would lead to putting the two documents into different distributions. However, its results were inaccurate in determining single-author documents because the test considered that every document already included a style change [35].