A puristic approach for joint dependency parsing and semantic role labeling

Abstract

We present a puristic approach for combining dependency parsing and semantic role labeling. In a first step, a data-driven strict incremental deterministic parser is used to compute a single syntactic dependency structure using a MEM trained on the syntactic part of the CoNLL 2008 training corpus. In a second step, a cascade of MEMs is used to identify predicates, and, for each found predicate, to identify its arguments and their types. All the MEMs used here are trained only with labeled data from the CoNLL 2008 corpus. We participated in the closed challenge, and obtained a labeled macro F1 for WSJ+Brown of 19.93 (20.13 on WSJ only, 18.14 on Brown). For the syntactic dependencies we got similar bad results (WSJ+Brown=16.25, WSJ= 16.22, Brown=16.47), as well as for the semantic dependencies (WSJ+Brown=22.36, WSJ=22.86, Brown=17.94). The current results of the experiments suggest that our risky puristic approach of following a strict incremental parsing approach together with the closed data-driven perspective of a joined syntactic and semantic labeling was actually too optimistic and eventually too puristic.