ESTIMATING SEMANTIC SIMILARITY IN YORUBA SENTENCES USING PATH-BASED METRICS

Abstract

Measuring semantic similarity among texts is an important task in many Natural Language Processing applications such as information retrieval, text summarization. However, there is dearth of work in the development of the tool for Yoruba Language, and this has therefore limited the advancement of Yoruba Language Engineering. This study addressed the gap by using a knowledge-based approach based on lexical resources. A total number of 434 nouns were collected from home-domain. The nouns were grouped into hypernym semantic classes. The classes were thereafter organized in hierarchy to form a taxonomy for Yoruba nouns and concepts. The model for the measurement of semantic similarity in Yoruba sentences was thereafter developed using path-based similarity measurement between the concepts represented in the taxonomy. Using the model, the system was implemented using python programming language. The developed system was evaluated using accuracy mean opinion score, and a score of 73.2% was achieved.