Existing word similarity measures are not robust to data sparseness since they rely only on the point estimation of words’ context profiles obtained from a limited amount of data.

In this study, we combined two clustering results (denoted as “sl+s2” in the results), each of which (“sl” and “s2”) has 2,000 hidden classes.4 We included this method since clustering can be regarded as another way of treating data sparseness .

In the NLP field, data sparseness has been recognized as a serious problem and tackled in the context of language modeling and supervised machine learning.

has been no study that seriously dealt with data sparseness in the context of semantic similarity calculation.

The data sparseness problem is usually solved by smoothing, regularization, margin maximization and so on (Chen and Goodman, 1998; Chen and Rosenfeld, 2000; Cortes and Vap-nik, 1995).

In order to increase the coverage even further and reduce the errors in lexicon construction, i.e., verb classification, caused by data sparseness , we need to devise a different method, perhaps using domain specific resources.

Other thematic roles did not perform well because of the data sparseness .

Data sparseness affected the linguistic schemata as well.