A DOP model for semantic interpretation

In data-oriented language processing, an annotated language corpus is used as a stochastic grammar. The most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way. This approach has been successfully used for syntactic analysis, using corpora with syntactic annotations such as the Penn Tree-bank. If a corpus with semantically annotated sentences is used, the same approach can also generate the most probable semantic interpretation of an input sentence. The present paper explains this semantic interpretation method. A data-oriented semantic interpretation algorithm was tested on two semantically annotated corpora: the English ATIS corpus and the Dutch OVIS corpus. Experiments show an increase in semantic accuracy if larger corpus-fragments are taken into consideration.