Gloss Overlap Extensions for a Semantic Network Algorithm: Building a Better Semantic Distance Measure

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Semantic similarity or inversely, semantic distance measures are useful in a variety of circumstances, from spell checking applications to a lightweight replacement for parsing within a natural language engine. Within this work, we examine the Jiang and Conrath's semantic distance algorithm; evaluated by Budanitsky as being the best performing, and subject the algorithm to a series of tests. We also propose a novel technique which corrects a crucial weakness of the original algorithm, and show that its application improves semantic distance measures for cases where the underlying linguistic network causes deficiencies