  This paper presents a semantic parsing approach for unrestricted texts.
Semantic parsing is one of the major bottlenecks of Natural Language
Understanding (NLU) systems and usually requires building expensive resources
not easily portable to other domains. Our approach obtains a case-role
analysis, in which the semantic roles of the verb are identified. In order to
cover all the possible syntactic realisations of a verb, our system combines
their argument structure with a set of general semantic labelled diatheses
models. Combining them, the system builds a set of syntactic-semantic patterns
with their own role-case representation. Once the patterns are build, we use an
approximate tree pattern-matching algorithm to identify the most reliable
pattern for a sentence. The pattern matching is performed between the
syntactic-semantic patterns and the feature-structure tree representing the
morphological, syntactical and semantic information of the analysed sentence.
For sentences assigned to the correct model, the semantic parsing system we are
presenting identifies correctly more than 73% of possible semantic case-roles.
