Combining LAPIS and WordNet for Learning of LR Parsers with Optimal Semantic Constraints

There is a history of research focussed on learning of shift-reduce parsers from syntactically annotated corpora by the means of machine learning techniques based on logic. The presence of lexical semantic tags in the treebank has proved useful for the learning of semantic constraints limiting the amount of nondeterminism in the parsers. The grain of the semantic tags used is of direct importance to that task. The combination of the system Lapis with the lexical resource WordNet described here allows to learn parsers while shifting the responsiblility for the choice of semantic tags from the corpus annotator to the learning system. The method is tested on an original corpus also described herein.