The overall goal of the field of natural language processing is to facilitate the
communication between humans and computers, and to help humans with natural language problems such as translation. In this thesis, we focus on semantic
language processing. Modelling semantics – the meaning of natural language –
requires both a structure to hold the semantic information and a device that can
enforce rules on the structure to ensure well-formed semantics while not being
too computationally heavy. The devices used in natural language processing
are preferably weighted to allow for comparison of the alternative semantic
interpretations outputted by a device.
The structure employed here is the abstract meaning representation (AMR).
We show that AMRs representing well-formed semantics can be generated while
leaving out AMRs that are not semantically well-formed. For this purpose, we
use a type of graph grammar called contextual hyperedge replacement grammar
(CHRG). Moreover, we argue that a more well-known subclass of CHRG –
the hyperedge replacement grammar (HRG) – is not powerful enough for AMR
generation. This is due to the limitation of HRG when it comes to handling
co-references, which in its turn depends on the fact that HRGs only generate
graphs of bounded treewidth.
Furthermore, we also address the N best problem, which is as follows: Given
a weighted device, return the N best (here: smallest-weighted, or more intuitively, smallest-errored) structures. Our goal is to solve the N best problem
for devices capable of expressing sophisticated forms of semantic representations
such as CHRGs. Here, however, we merely take a first step consisting in
developing methods for solving the N best problem for weighted tree automata
and some types of weighted acyclic hypergraphs.