Abstract
As a first step of word sense disambiguation (WSD) errors analysis, generally we need investigate the causes of errors and classify them. For this purpose, seven analysts classified the error data for analysis from their unique standpoints. Next, we attempted to merge the results from the analyses. However, merging these results through discussions was difficult because the results differed significantly. Therefore, we used a clustering method for a certain level of automatic merger. Consequently, we classified WSD errors into nine types, and it turned out that the three main types of errors covers 90% of the total WSD errors. Moreover, we showed that the merged error types represented seven results and was standardized by defining the similarity between two classifications and comparing it with each analysis result.
