Measure Term Similarity Using a Semantic Network Approach

Abstract

Computing semantic similarity between two words comes with a variety of approaches. This is mainly essential
for applications such as text analysis and text understanding. In traditional systems, search engines are used
to compute the similarity between words. In that sense, search engines are keyword-based. There is one
drawback that users should know what exactly they are looking for. There are mainly two main approaches for
computation, namely knowledge-based and corpus-based approaches. However, there is one drawback that these
two approaches are not suitable for computing similarity between multiword expressions. This system provides an
efficient and effective approach for computing term similarity using a semantic network approach. A clustering
approach is used in order to improve the accuracy of the semantic similarity. This approach is more efficient than
other computing algorithms. This technique can also be applied to large-scale datasets to compute term similarity.