Though the lack of semantic representation of automatically extracted LTAGs is an obstacle in using these formalism, due to the advent of some powerful statistical parsers that were trained on them, these grammars have been taken into consideration more than before. Against of this grammatical class, there are some widely usage manually crafted LTAGs that are enriched with semantic representation but suffer from the lack of efficient parsers. The available representation of latter grammars beside the statistical capabilities of former encouraged us in constructing a link between them.
Here, by focusing on the automatically extracted LTAG used by MICA  and the manually crafted English LTAG namely XTAG grammar , a statistical approach based on HMM is proposed that maps each sequence of former elementary trees onto a sequence of later elementary trees. To avoid of converging the HMM training algorithm in a local optimum state, an EM-based learning process for initializing the HMM parameters were proposed too. Experimental results show that the mapping method can provide a satisfactory way to cover the deficiencies arises in one grammar by the available capabilities of the other.
Springer Berlin/Heidelberg, 2011. 68-81 p.