EuroMatrixPlus project funded by the European Commission, 7th Framework ProgrammeParsing is the task of assigning syntactic or semantic structure to a natural language
sentence. This thesis focuses on syntactic parsing with Combinatory Categorial Grammar
(CCG; Steedman 2000). CCG allows incremental processing, which is essential
for speech recognition and some machine translation models, and it can build semantic
structure in tandem with syntactic parsing. Supertagging solves a subset of the parsing
task by assigning lexical types to words in a sentence using a sequence model. It has
emerged as a way to improve the efficiency of full CCG parsing (Clark and Curran,
2007) by reducing the parser’s search space. This has been very successful and it is the
central theme of this thesis.
We begin by an analysis of how efficiency is being traded for accuracy in supertagging.
Pruning the search space by supertagging is inherently approximate and to contrast
this we include A* in our analysis, a classic exact search technique. Interestingly,
we find that combining the two methods improves efficiency but we also demonstrate
that excessive pruning by a supertagger significantly lowers the upper bound on accuracy
of a CCG parser.
Inspired by this analysis, we design a single integrated model with both supertagging
and parsing features, rather than separating them into distinct models chained
together in a pipeline. To overcome the resulting complexity, we experiment with both
loopy belief propagation and dual decomposition approaches to inference, the first empirical
comparison of these algorithms that we are aware of on a structured natural
language processing problem.
Finally, we address training the integrated model. We adopt the idea of optimising
directly for a task-specific metric such as is common in other areas like statistical
machine translation. We demonstrate how a novel dynamic programming algorithm
enables us to optimise for F-measure, our task-specific evaluation metric, and experiment
with approximations, which prove to be excellent substitutions.
Each of the presented methods improves over the state-of-the-art in CCG parsing.
Moreover, the improvements are additive, achieving a labelled/unlabelled dependency
F-measure on CCGbank of 89.3%/94.0% with gold part-of-speech tags, and
87.2%/92.8% with automatic part-of-speech tags, the best reported results for this task
to date. Our techniques are general and we expect them to apply to other parsing problems,
including lexicalised tree adjoining grammar and context-free grammar parsing
