SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts
This corpus addresses a critical gap in Sinhala legal research by providing a high-quality, machine-readable dataset for NLP tasks like summarization and information extraction, which is significant for researchers and practitioners working with Sinhala legal texts.
This paper introduces SinhaLegal, a Sinhala legislative text corpus of approximately 2 million words from 1,206 legal documents (Acts and Bills). The corpus was created by extracting text using OCR and extensive manual cleaning, and its quality was evaluated through corpus statistics, lexical diversity, word frequency, named entity recognition, topic modeling, and perplexity analysis with language models.
SinhaLegal introduces a Sinhala legislative text corpus containing approximately 2 million words across 1,206 legal documents. The dataset includes two types of legal documents: 1,065 Acts dated from 1981 to 2014 and 141 Bills from 2010 to 2014, which were systematically collected from official sources. The texts were extracted using OCR with Google Document AI, followed by extensive post-processing and manual cleaning to ensure high-quality, machine-readable content, along with dedicated metadata files for each document. A comprehensive evaluation was conducted, including corpus statistics, lexical diversity, word frequency analysis, named entity recognition, and topic modelling, demonstrating the structured and domain-specific nature of the corpus. Additionally, perplexity analysis using both large and small language models was performed to assess how effectively language models respond to domain-specific texts. The SinhaLegal corpus represents a vital resource designed to support NLP tasks such as summarisation, information extraction, and analysis, thereby bridging a critical gap in Sinhala legal research.