# Automatic Era Identification in Classical Arabic Poetry
## 2 Background and Related Work  
### Text Categorization and Authorship Attribution  
Authorship attribution (AA) is a text classification task, where the goal is to assign a given text to one possible author out of a selection of pre-specified candidate authors of that text [13; 14]. A main application of AA is to automatically identify the authors of disputed or anonymous texts. A well-known study of AA examined the authenticity of plays by [PERSON] [15]. Another key work in this field by [PERSON] and [PERSON] [16] studied the mystery of the authorship of the Federalist Papers. While word information serves as the main evidence in classifying texts into topical categories, it has been shown that authorship is strongly indicated by writing style [17]. Distinctive elements of personal styles include sentence and word length, typical word sequences [14] and usage patterns of function words [18; 19]. [PERSON] et al. [15] found classification using support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed by [PERSON] [20]) and Bayesian regression (Bayesian regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables) to give good results in AA, where they also processed the text into word classes derived from systemic functional linguistics [21] and character n-grams (sequences of characters that appear in words). More recently, exploring the task of the AA of Polish novels, [PERSON] et al. [22] suggested that part-of-speech (POS) tag sequences in text were informative.  
In our work, we aim to determine the origin of a poem with respect to its writing era rather than its authorship. Nevertheless, our general approach is similar, where we model additional specialized features that describe the topical and stylistic elements of the target domain. The paradigm of topic modeling aims to discover abstract topics from a collection of documents, which correspond to clusters of words that tend to co-occur within local contexts such as a document or a paragraph [23]. Given the mappings between individual words and topics, a document may be represented as a distribution over topics. Previously, [PERSON] et al. [24] successfully applied topic modeling for AA, outperforming competitive approaches. In this work, we infer the topics that underlie the poems in our dataset, representing each poem as a distribution over those topics. We assume and show that there is correlation between the topics depicted in a poem and the period in which it was authored.  
Methodology-wise, similar to the works mentioned above, we apply supervised classification and training era prediction models from labeled examples, for which the true category (era) is of high quality. Concretely, we utilize the poems with certain authorship as the labeled data. Having modeled the poems in terms of a variety of features, the classification algorithm selects, weights, and combines feature information that is found to provide meaningful evidence towards label prediction in the training process. Classification performance is then evaluated using labeled examples that have been set-aside, i.e., examples that have not been used in the training process. The evaluation measures include the following: precision, defined as the percentage of correctly classified items out of all of the items assigned to a target class (true positive/(true positive + false positive)); recall, defined as the percentage of correctly identified items out of the items that truly belong to a target category (true positive/(true positive + false negative)); and F-measure, which is the harmonic mean of precision and recall [25]. Once the models are tuned based on the evaluation results, they can be applied to texts with an uncertain label--authorship or era, per this study.