Mink: An Incremental Data-Driven Dependency Parser with Integrated Conversion to Semantics

Abstract

While there are several data-driven dependency parsers, there is still a gap with regards to incrementality. However, as shown in Brick and Scheutz [3], incremental processing is necessary in human-robot interaction. As is shown in Nivre et al. [12], dependency parsing is well-suited for mostly incremental processing. However, there is as of yet no dependency parser that combines syntax and semantics by including traditional dependency parsing, CCG tagging, and lambdalogical structures in one fast, accurate application suitable for embodied natural language processing. This paper addresses that gap by introducing Mink, an incremental data-driven dependency parser with integrated conversion to semantics. We show that Mink is comparable to similar but non-incremental parsers, and that it succeeds at performing some semantic analysis.