Abstrakt
Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, inspired by Head-driven Phrase Structure Grammar (HPSG). We thus refer to this joint parsing of constituency and dependency as HPSG-like parsing.
However, in HPSG-like parsing, decoding this unified grammar has a higher time complexity (O(n(3))) than decoding either form individually (O(n(3))) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in O(n(3)) time complexity.
Furthermore, on the basis of this proposed practical HPSG-like parser, we investigated the strengths of HPSG-like parsing and explored the general method of training an HPSG-like parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG-like parsing.