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The formalized and algorithmic study of human language within the field of Natural Language Processing (NLP) has motivated much theoretical work in the related field of formal languages, in particular the subfields of grammar and automata theory. Motivated and informed by NLP, the papers in this thesis explore the connections between expressibility – that is, the ability for a formal system to define complex sets of objects – and algorithmic complexity – that is, the varying amount of effort required to analyse and utilise such systems. Our research studies formal systems working not just on strings, but on more complex structures such as trees and graphs, in particular syntax trees and semantic graphs. The field of mildly context-sensitive languages concerns attempts to find a useful class of formal languages between the context-free and context-sensitive. We study formalisms defining two candidates for this class; tree-adjoining languages and the languages defined by linear context-free rewriting systems. For the former, we specifically investigate the tree languages, and define a subclass and tree automaton with linear parsing complexity. For the latter, we use the framework of parameterized complexity theory to investigate more deeply the related parsing problems, as well as the connections between various formalisms defining the class. The field of semantic modelling aims towards formally and accurately modelling not only the syntax of natural language statements, but also the meaning. In particular, recent work in semantic graphs motivates our study of graph grammars and graph parsing. To the best of our knowledge, the formalism presented in Paper III of this thesis is the first graph grammar where the uniform parsing problem has polynomial parsing complexity, even for input graphs of unbounded node degree.

The field of semantic modelling concerns formal models for semantics, that is, formal structures for the computational and algorithmic processing of meaning. This thesis concerns formal graph languages motivated by this field. In particular, we investigate two formalisms: Order-Preserving DAG Grammars (OPDG) and Order-Preserving Hyperedge Replacement Grammars (OPHG), where OPHG generalise OPDG.

Graph parsing is the practise of, given a graph grammar and a graph, to determine if, and in which way, the grammar could have generated the graph. If the grammar is considered fixed, it is the non-uniform graph parsing problem, while if the grammars is considered part of the input, it is named the uniform graph parsing problem. Most graph grammars have parsing problems known to be NP-complete, or even exponential, even in the non-uniform case. We show both OPDG and OPHG to have polynomial uniform parsing problems, under certain assumptions.

We also show these parsing algorithms to be suitable, not just for determining membership in graph languages, but for computing weights of graphs in graph series.

Additionally, OPDG is shown to have several properties common to regular languages, such as MSO definability and MAT learnability. We moreover show a direct corresponcence between OPDG and the regular tree grammars.

Finally, we present some limited practical experiments showing that real-world semantic graphs appear to mostly conform to the requirements set by OPDG, after minimal, reversible processing.