Named Entity Recognition (NER) has been a core task of natural language processing (NLP) since the Message Understanding Conferences (MUCs). Data annotation plays a crucial role in this task. However, existing annotation studies often rely on the literal sense of entities. Such annotations may lead to inconsistencies, while resolving ambiguity introduced by figurative tropes like metonymy. For example, in India won the series, India refers to a sports team instead of a geographic location. Understanding such non-literal senses is crucial for various NLP applications such as Question Answering, Information Extraction, etc. By addressing this gap, this study presents an annotation framework and detailed guidelines for annotating metonymic readings of named entities in Marathi, an Indo-Aryan language spoken in the central-western region of India. The study uses news corpus from various domains. It presents a two-tiered annotation framework for annotating conventional metonymies in Marathi language. Further, it describes the annotation framework applied to a corpus of 1,279 Marathi sentences. The result shows the inadequacy of literal-only annotation as 53.6% of named entity spans have metonymic readings. This study makes a crucial contribution for resource development for low-resource languages that share similar linguistic structures and cultural contexts. The paper describes the framework with necessary examples, challenges and concludes with a future scope.

Metonymy, often considered as a figurative trope, is a frequently occurring linguistic phenomenon in which an entity is replaced by a semantically related entity. Named entities are commonly used to refer to associated concepts. For instance, in the sentence India signed a treaty, the geographical name India stands metonymically for the government rather than the physical location. This study develops a hybrid architecture to classify literal and metonymic usages of named entities in Marathi language using small data. The approach integrates Pustejovsky’s Generative Lexicon framework with linguistic features, including part-of-speech tags, named entity labels, and lemmas. The model is evaluated on 890 sentences and achieved F1 scores of 66.98% and 71.97% for literal and metonymic instances, respectively. The study highlights the effectiveness of the features in capturing metonymic contexts, though precision remains a target for improvement. Ablation results confirm that the Formal and Constitutive Qualia roles are the most critical components for detecting metonymic shifts, while the Telic role introduces modest noise in the present corpus. This experiment shows the scope for developing hybrid models for learning non-literal language using small data, which could be beneficial for less-explored and low-resource languages.

Addressing tasks in Natural Language Processing requires access to sufficient and high-quality data. However, working with languages that have limited resources poses a significant challenge due to the absence of established methodologies, frameworks, and collaborative efforts. This paper intends to briefly outline the challenges associated with standardization in data creation, focusing on Indian languages, which are often categorized as low resource languages. Additionally, potential solutions and the importance of standardized procedures for low-resource language data are proposed. Furthermore, the critical role of standardized protocols in corpus creation and their impact on research is highlighted. Lastly, this paper concludes by defining what constitutes a corpus.