![]() ![]() In this paper, we describe initial experiments following an approach based on unsupervised learning of morphology from a text corpus, especially developed for this purpose. Developing a knowledge base of legal words and morphological rules is an important task in computational linguistics. Usually, a capable system-human or machine, knows a subset of the entire vocabulary of a language and morphological rules to determine attributes of words not seen before. Usually, a natural language understanding system either already knows the words that appear in the text, or is able to automatically learn relevant information about a word upon encountering it. Words play a crucial role in aspects of natural language understanding such as syntactic and semantic processing.
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