Calculation of textual similarity using semantic relatedness function

Finding the similarity between two sentences is an essential task in different fields such as natural language processing (NLP) and information retrieval (IR). Semantic relatedness similarity between two sentences is concerned with measuring how two sentences share the same meaning. Over the last decade, different methods for measuring sentence similarity have been proposed in the literature. Some methods use word semantic relatedness function in sentence similarity calculations. This thesis aims to compare these methods using four data sets selected from different fields, providing a testable of a various range of writing expressions to challenge the selected methods. Results show that the use of corpus-based word semantic similarity function has significantly outperformed that of WordNet-based word semantic similarity function in sentence similarity methods. Moreover, we propose a new sentence similarity measure method by extending an existing method in the literature called Overall similarity. Furthermore, the results show that the proposed method has significantly improved the performance of the Overall method. All the selected methods are tested and compared with other state-of-the-art methods.