Bottom Up Tagset Design from Maximally Reduced Tagset

For highly innectional languages, where the number of morpho-syntactic descriptions (MSD) is very high, the use of a reduced tagset is crucial for reasons of implementation problems as well as the problem of sparse data. The standard procedure is to start from the large set of MSDs incorporating all morphosyntactic features and design a reduced tagset by eliminating the attributes which play no role in disambiguation. This paper presents the opposite approach which using a greedy algorithm maximally reduces a tagset without loss of information, and instead of elimination, reintroduces features. This process can arrive at a very small tagset and result in accuracy comparable to that achieved with larger tagsets designed by elimination. The language model based on the reduced tagset needs fewer parameters and training time decreases signiicantly.