Combinatory categorial grammar (CCG) is a grammar formalism used for natural language parsing. CCG assigns structured lexical categories to words and uses a small set of combinatory rules to combine these categories to parse a sentence. In this work we propose and implement a new approach to CCG parsing that relies on a prominent knowledge representation formalism, answer set programming (ASP) - a declarative programming paradigm. We formulate the task of CCG parsing as a planning problem and use an ASP computational tool to compute solutions that correspond to valid parses. Compared to other approaches, there is no need to implement a specific parsing algorithm using such a declarative method. Our approach aims at producing all semantically distinct parse trees for a given sentence. From this goal, normalization and efficiency issues arise, and we deal with them by combining and extending existing strategies. We have implemented a CCG parsing tool kit - AspCcgTk - that uses ASP as its main computational means. The C&C supertagger maybe used as a preprocessor within AspCcgTk that allows us to achieve wide-coverage natural language parsing.

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Correct Reasoning: Essays on Logic-based AI (2012). Springer.

@article{lier12fest, title={Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming}, author={Yuliya Lierler and Peter Schueller}, booktitle={Correct Reasoning: Essays on Logic-based AI}, publisher={Springer}, url="http://www.cs.utexas.edu/users/ai-lab?lier12fest", year={2012} }