Enhancing Sumerian Lemmatization by Unsupervised Named-Entity Recognition

Abstract Lemmatization for the Sumerian language, compared to the modern languages, is much more challenging due to that it is a long dead language, highly skilled language experts are extremely scarce and more and more Sumerian texts are coming out. This paper describes how our unsupervised Sumerian named-entity recognition (NER) system helps to improve the lemmatization of the Cuneiform Digital Library Initiative (CDLI), a specialist database of cuneiform texts, from the Ur III period. Experiments show that a promising improvement in personal name annotation in such texts and a substantial reduction in expert annotation effort can be achieved by leveraging our system with minimal seed annotation