In many natural language processing based intelligent systems, parsing is the first task to perform. However, in the next stages, many systems often have the capacity of processing a limited number of parsed structures. The problem is to determine what parsed sentences can be recognized by a system. The decision of syntactic structures which can be processed by a system is consider as the task of "classification" of a parsed sentence into one of given classes of recognizable parses. In this paper we deal with this issue by proposing a method for mapping Vietnamese chunked sentences to a set of pre-defined shallow structures. Also, we tag lexicons and chunk phrases of the original sentences using our Functional Part-of-Speech (FPOS) tagset with Apache OpenNLP tools (Tokenizer, POS Tagger, Chunker). Based on the foundation of Functional Grammar, we define new lexical tags and combine with Penn-Treebank tagset to build our FPOS tagset. Due to our set of shallow structures is finite, instead of using a parser, we propose a rule-based algorithm for the mapping process. We establish conversion rules according to the reality experiences when using Vietnamese in common communication. The experiment shows that we converse successfully for the major of testing sentences and the algorithm can be applied for different languages.