## 2 Related Work  
We provide a brief survey considering works carried out in different languages. [PERSON] et al. [4] explored various deep learning architectures on different corpora. Their experimental results showed that the article-level GRU obtained a maximum accuracy of 69.1% and 89.2% on the C50 and Gutenberg corpora, respectively. [PERSON] et al. [5] used a deep learning architecture to extract features from articles based on different character-based n-gram approaches. They also investigated the application of various feature extraction and selection approaches using a stacked denoising autoencoder and support vector machines to build the classifier. [PERSON] et al. [6] proposed a semantic relationship-based unsupervised method for identifying the writing pattern of different authors using principal component analysis and linear discriminant analysis based on expression, term relevancy, and nonsubject stylistic term from different articles. [PERSON] et al. [7] used the word2 vec word embeddings model, which provides semantic relationships between words, along with the multilayer perceptron (MLP) classification algorithm. The experimental results showed an accuracy of 95.83% on the PAN 2012 dataset [8], indicating a better performance of the system when compared to other standard approaches. [PERSON] et al. [9] used the LDA model with n-grams and cosine similarity for identifying authors in English and Urdu documents. They obtained overall accuracies of 84.52% and 93.17% on the PAN 2012 dataset [8] and Urdu news articles. [PERSON] et al. [10] conducted two experiments, firstly comparing the decisions using content and stylometric features and secondly, describing the process and the features on which their judgment was based. [PERSON] et al. [11] trained a decision tree (J48 learning algorithm) using text-based features for identifying different authors of poems. [PERSON] et al. [12] worked with word and character unigram features and a support vector machine (SVM) classifier on the C50 dataset and achieved 88% accuracy. [PERSON] et al. [13] also used an SVM along with the bag-of-terms model, obtaining 80.80% accuracy on the C50 dataset for the author identification task. [PERSON] et al. [14] provided a brief discussion on the results of the four shared tasks arranged at the PAN 2020 [15] lab on digital text forensics and stylometry. This involved author profiling, cross-domain author verification, celebrity profiling, and style change detection. The aim was to advance the existing approaches and evaluate them on new standard datasets. [PERSON] and [PERSON] [16] worked with stylometric features to overcome the limitations of having only n-gram features for Urdu texts; their experimental results showed an accuracy of 94.03%, indicating a discriminative power of the stylometric features used. [PERSON] and [PERSON] [17] tested three different graph-based algorithms and, for each algorithm, the graphs were clustered, and the weights were assigned based on the graph traversal method. Experiments were done using articles from six authors obtaining a maximum accuracy of 94.98%. Using SMO along with the J48 algorithm, [PERSON] and [PERSON] [18] implemented two techniques that used lexical and stylistic feature extraction processes for identifying authors from 15 philosophical articles written in Marathi by five authors, achieving an accuracy of 80%. [PERSON] et al. [19] used semantic-based and stylistic-based features and the support vector machine algorithm for identifying poems from four genres and achieved an accuracy of 92.3%. [PERSON] and [PERSON] [20] proposed a hybrid model by combining word n-grams with the naive Bayes (NB) algorithm showing an encouraging accuracy of 95%. A brief analysis of the literature study is provided in the following Table 1.  
The literature study reveals that various researchers have paid interest to author identification tasks. However, they have mainly focused on the English language [21; 22; 23; 24; 25].