This paper focuses on combining answers generated by a semantic parser that produces semantic role labels (SRLs) and those generated by syntactic parser that produces function tags for answering 5-W questions, i.e., who, what, when, where,
and why. We take a probabilistic approach in which a system’s ability to correctly answer 5-W questions is measured with the likelihood that its answers are produced for the given word sequence. This is achieved by training statistical language models (LMs) that are used to predict whether the answers returned
by semantic parse or those returned by the syntactic parser are more likely. We evaluated our approach using the OntoNotes dataset. Our experimental results indicate that the proposed LM-based combination strategy was able to improve the performance of the best individual system in terms of both F1 measure and accuracy. Furthermore, the error rates for each question type were also significantly reduced with the help of the proposed approach.