The Romanized text has become popular with the growth of digital communication platforms, largely due to the familiarity with English keyboards. In Sri Lanka, Romanized Sinhala, commonly referred to as “Singlish” is widely used in digital communications. This paper introduces a novel context-aware back-transliteration system designed to address the ad-hoc typing patterns and lexical ambiguity inherent in Singlish. The proposed system com bines dictionary-based mapping for Singlish words, a rule-based transliteration for out of-vocabulary words and a BERT-based language model for addressing lexical ambiguities. Evaluation results demonstrate the robustness of the proposed approach, achieving high BLEU scores along with low Word Error Rate (WER) and Character Error Rate (CER) across test datasets. This study provides an effective solution for Romanized Sinhala back-transliteration and establishes the foundation for improving NLP tools for similar low-resourced languages.

This paper presents the development of CHAMUÇA, a novel lexical resource designed to document the influence of the Portuguese language on various Asian languages, with an initial focus on the languages of South Asia. Through the utilization of linked open data and the OntoLex vocabulary, CHAMUÇA offers structured insights into the linguistic characteristics, and cultural ramifications of Portuguese borrowings across multiple languages. The article outlines CHAMUÇA’s potential contributions to the linguistic linked data community, emphasising its role in addressing the scarcity of resources for lesser-resourced languages and serving as a test case for organising etymological data in a queryable format. CHAMUÇA emerges as an initiative towards the comprehensive catalogization and analysis of Portuguese borrowings, offering valuable insights into language contact dynamics, historical evolution, and cultural exchange in Asia, one that is based on linked data technology.

Relationship extraction from unstructured data remains one of the most challenging tasks in the field of Natural Language Processing (NLP). The complexity of relationship extraction arises from the need to comprehend the underlying semantics, syntactic structures, and contextual dependencies within the text. Unstructured data poses challenges with diverse linguistic patterns, implicit relationships, contextual nuances, complicating accurate relationship identification and extraction. The emergence of Large Language Models (LLMs), such as GPT (Generative Pre-trained Transformer), has indeed marked a significant advancement in the field of NLP. In this work, we assess and evaluate the effectiveness of LLMs in relationship extraction in the Holocaust testimonies within the context of the Historical realm. By delving into this domain-specific context, we aim to gain deeper insights into the performance and capabilities of LLMs in accurately capturing and extracting relationships within the Holocaust domain by developing a novel knowledge graph to visualise the relationships of the Holocaust. To the best of our knowledge, there is no existing study which discusses relationship extraction in Holocaust testimonies. The majority of current approaches for Information Extraction (IE) in historic documents are either manual or OCR based. Moreover, in this study, we found that the Subject-Object-Verb extraction using GPT3-based relations produced more meaningful results compared to the Semantic Role labeling-based triple extraction.