Poetry represents the oldest and most esteemed literary form, allowing poets to convey ideas while carefully attending to elements such as meaning, coherence, poetic quality, and fluency. Notably, the creation of good poetry entails considerations of rhyme and meter. With the advent of artificial intelligence (AI), significant advancements have been made in automatic text generation, primarily within languages such as English and Chinese. However, the generation of Urdu poetry presents a unique challenge due to the language’s inherent ambiguity, cultural and historical nuances, and the demand for creativity. The existing body of literature has only marginally explored Urdu prose and has almost entirely overlooked the domain of Urdu poetry generation, primarily due to the scarcity of comprehensive training data. In response to this deficiency, this research endeavor addresses this challenge. It begins by introducing a specialized Urdu poetry dataset adhering to a specific meter, “behr-e-khafeef,” which incorporates approximately 20,000 couplets from the Rekhta repository. Subsequently, a character-based encoding methodology is proposed to transform these couplets into a numerical representation, assigning a distinct identifier to each character. The generation process initiates with the creation of the first verse through a character-level LSTM, followed by the application of a machine translation technique, specifically sequence-to-sequence learning, to formulate the second verse based on the first. The generated poetry is subjected to evaluation based on metrics, including BLEU scores. Additionally, an expert panel of Urdu poets is engaged to conduct a human assessment of the generated couplets, with the evaluation encompassing critical dimensions such as meaning, coherence, poetic quality, and fluency. Our findings are juxtaposed with existing poetry generation systems, demonstrating a notable advancement in the state-of-the-art, as evidenced by a BLEU score of 0.23. The research culminates with the presentation of prospective avenues for further exploration, aimed at inspiring the scholarly community to enhance the domain of poetry generation and augment existing contributions in this field.

Abstract Traditional image retrieval methods often face challenges in adapting to varying user preferences and dynamic datasets. To address these limitations, this research introduces a novel image retrieval framework utilizing deep deterministic policy gradients (DDPG) augmented with a self-adaptive reward mechanism (SARM). The DDPG-SARM framework dynamically adjusts rewards based on user feedback and retrieval context, enhancing the learning efficiency and retrieval accuracy of the agent. Key innovations include dynamic reward adjustment based on user feedback, context-aware reward structuring that considers the specific characteristics of each retrieval task, and an adaptive learning rate strategy to ensure robust and efficient model convergence. Extensive experimentation with the three distinct datasets demonstrates that the proposed framework significantly outperforms traditional methods, achieving the highest retrieval accuracy having 3.38%, 5.26%, and 0.21% improvement overall as compared to the mainstream models over DermaMNIST, PneumoniaMNIST, and OrganMNIST datasets, respectively. The findings contribute to the advancement of reinforcement learning applications in image retrieval, providing a user-centric solution adaptable to various dynamic environments. The proposed method also offers a promising direction for future developments in intelligent image retrieval systems.

Authorship attribution involves determining the original author of an anonymous text from a pool of potential authors. The author attribution task has applications in several domains, such as plagiarism detection, digital text forensics, and information retrieval. While these applications extend beyond any single language, existing research has predominantly centered on English, posing challenges for application in languages such as Sinhala due to linguistic disparities and a lack of language processing tools. We present the first comprehensive study on cross-topic authorship attribution for Sinhala texts and propose a solution that can effectively perform the authorship attribution task even if the topics within the test and training samples differ. Our solution consists of three main parts: (i) extraction of topic-independent stylometric features, (ii) generation of a small candidate author set with the help of similarity search, and (iii) identification of the true author. Several experimental studies were carried out to demonstrate that the proposed solution can effectively handle real-world scenarios involving a large number of candidate authors and a limited number of text samples for each candidate author.

The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.

Abstract Most existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in this under‐resourced language. We develop an open‐source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. The objectives of this work are bi‐fold (a) the creation of a human‐annotated corpus for the research of sentiment analysis in Urdu; and (b) measurement of up‐to‐date model performance using a corpus. For their assessment, we performed binary and ternary classification studies utilizing another model, namely long short‐term memory (LSTM), recurrent convolutional neural network (RCNN) Rule‐Based, N‐gram, support vector machine , convolutional neural network, and LSTM. The RCNN model surpasses standard models with 84.98% accuracy for binary classification and 68.56% accuracy for ternary classification. To facilitate other researchers working in the same domain, we have open‐sourced the corpus and code developed for this research.

Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

Native Language Identification (NLI) aims at identifying the native languages of authors by analyzing their text samples written in a non-native language. Most existing studies investigate this task for educational applications such as second language acquisition and require the learner corpora. This article performs NLI in a challenging context of the user-generated-content (UGC) where authors are fluent and advanced non-native speakers of a second language. Existing NLI studies with UGC (i) rely on the content-specific/social-network features and may not be generalizable to other domains and datasets, (ii) are unable to capture the variations of the language-usage-patterns within a text sample, and (iii) are not associated with any outlier handling mechanism. Moreover, since there is a sizable number of people who have acquired non-English second languages due to the economic and immigration policies, there is a need to gauge the applicability of NLI with UGC to other languages. Unlike existing solutions, we define a topic-independent feature space, which makes our solution generalizable to other domains and datasets. Based on our feature space, we present a solution that mitigates the effect of outliers in the data and helps capture the variations of the language-usage-patterns within a text sample. Specifically, we represent each text sample as a point set and identify the top- k stylistically similar text samples (SSTs) from the corpus. We then apply the probabilistic k nearest neighbors’ classifier on the identified top- k SSTs to predict the native languages of the authors. To conduct experiments, we create three new corpora where each corpus is written in a different language, namely, English, French , and German . Our experimental studies show that our solution outperforms competitive methods and reports more than 80% accuracy across languages.

Stylometry is the statistical analyses of variationsin the author's literary style. The technique has been used inmany linguistic analysis applications, such as, author profiling, authorship identification, and authorship verification. Over thepast two decades, authorship identification has been extensivelystudied by researchers in the area of natural language processing. However, these studies are generally limited to (i) a small number of candidate authors, and (ii) documents with similar lengths. In this paper, we propose a novel solution by modeling authorship attribution as a set similarity problem to overcome the two stated limitations. We conducted extensive experimental studies on a real dataset collected from an online book archive, Project Gutenberg. Experimental results show that in comparison to existing stylometry studies, our proposed solution can handlea larger number of documents of different lengths written by alarger pool of candidate authors with a high accuracy.