Dynamic Programming Algorithms for Semantic Dependency Parsing

Abstract: Dependency parsing can be a useful tool to allow computers to parse text. In 2015, Kuhlmann and Jonsson proposed a logical deduction system that parsed to non-crossing dependency graphs with an asymptotic time complexity of O(n3), where “n” is the length of the sentence to parse. This thesis extends the deduction system by Kuhlmann and Jonsson; the extended deduction system introduces certain crossing edges, while maintaining an asymptotic time complexity of O(n4). In order to extend the deduction system by Kuhlmann and Jonsson, fifteen logical item types are added to the five proposed by Kuhlmann and Jonsson. These item types allow the deduction system to intro-duce crossing edges while acyclicity can be guaranteed. The number of inference rules in the deduction system is increased from the 19 proposed by Kuhlmann and Jonsson to 172, mainly because of the larger number of combinations of the 20 item types. The results are a modest increase in coverage on test data (by roughly 10% absolutely, i.e. approx. from 70% to 80%), and a comparable placement to that of Kuhlmann and Jonsson by the SemEval 2015 task 18 metrics. By the method employed to introduce crossing edges, derivational uniqueness is impossible to maintain. It is hard to defien the graph class to which the extended algorithm, QAC, parses, and it is therefore empirically compared to 1-endpoint crossing and graphs with a page number of two or less, compared to which it achieves lower coverage on test data. The QAC graph class is not limited by page number or crossings. The takeaway of the thesis is that extending a very minimal deduction system is not necessarily the best approach, and that it may be better to start off with a strong idea of to which graph class the extended algorithm should parse. Additionally, several alternative ways of extending Kuhlmann and Jonsson are proposed.