Language Processing:

Tokenization: Break down the Myanmar text data into smaller units like words or characters. Libraries like spaCy offer built-in tokenization functions for Burmese text. Normalization: Ensure consistency in the data by converting all text to lowercase, removing punctuation, and handling diacritics (accents) appropriately. Stemming/Lemmatization: Reduce words to their root forms (stems) or dictionary base forms (lemmas) to improve model efficiency. Specific libraries like PyStemmer might be needed for Burmese stemming. Named Entity Recognition (NER): Identify and classify named entities like legal terms, companies, and individuals within the legal documents. This can be crucial for tasks like contract analysis.