Existing Persian named entity recognition (NER) research has focused predominantly on news and social media domains, leaving literary texts—with their distinct linguistic characteristics—virtually unexplored. This paper addresses this gap by developing a new literary NER corpus using the Persian translation of The Little Prince story and evaluating existing state-of-the-art Persian NER tools on this corpus, trained exclusively on news and social media corpora. Our analysis reveals significant performance degradation on literary text, identifying systematic errors related to narrative-specific entities, metaphorical language, and discourse structures that challenge conventional NER approaches.

Uniform Meaning Representation (UMR) has cross-linguistic design principles that make it particularly well-suited as a semantic representation framework for capturing all language-specific phenomena. Despite its growing adoption, no UMR corpus currently exists for Persian. In this paper, we present the first version of a Persian UMR dataset created through a rule-based conversion of existing Persian AMR annotations from The Little Prince corpus, followed by manual mapping of split semantic roles from AMR to their finer-grained UMR counterparts. We report detailed statistics on the conversion, analyze the challenges of mapping Persian AMR structures into UMR, and provide illustrative examples. The resource is freely available and it lays the groundwork for subsequent enrichment of Persian UMR with additional semantic layers, including co-reference, named entities, and discourse relations.

Ezafe is a grammatical particle in some Iranian languages that links two words together. Regardless of the important information it conveys, it is almost always not indicated in Persian script, resulting in mistakes in reading complex sentences and errors in natural language processing tasks. In this paper, we experiment with different machine learning methods to achieve state-of-the-art results in the task of ezafe recognition. Transformer-based methods, BERT and XLMRoBERTa, achieve the best results, the latter achieving 2.68% F1-score more than the previous state-of-the-art. We, moreover, use ezafe information to improve Persian part-of-speech tagging results and show that such information will not be useful to transformer-based methods and explain why that might be the case.

Words are properly segmented in the Persian writing system; in practice, however, these writing rules are often neglected, resulting in single words being written disjointedly and multiple words written without any white spaces between them. This paper addresses the problems of word segmentation and zero-width non-joiner (ZWNJ) recognition in Persian, which we approach jointly as a sequence labeling problem. We achieved a macro-averaged F1-score of 92.40% on a carefully collected corpus of 500 sentences with a high level of difficulty.

Identification of the languages written using cuneiform symbols is a difficult task due to the lack of resources and the problem of tokenization. The Cuneiform Language Identification task in VarDial 2019 addresses the problem of identifying seven languages and dialects written in cuneiform; Sumerian and six dialects of Akkadian language: Old Babylonian, Middle Babylonian Peripheral, Standard Babylonian, Neo-Babylonian, Late Babylonian, and Neo-Assyrian. This paper describes the approaches taken by SharifCL team to this problem in VarDial 2019. The best result belongs to an ensemble of Support Vector Machines and a naive Bayes classifier, both working on character-level features, with macro-averaged F1-score of 72.10%.