Supertagging with combinatory categorial grammar for dependency parsing

Akkuş, Burak Kerim
Combinatory Categorial Grammar (CCG) categories contain syntactic and semantic information. CCG derivation trees can be used in extracting partial dependency structures by providing the missing information in order to build complete dependency structures. Therefore, CCG categories are sometimes referred to as supertags. The amount of information encoded in supertags makes it possible to create very accurate and fast parsers as supertagging is considered ``almost parsing''. In this thesis, a maximum entropy based part of speech tagger is presented to improve the performance of CCG supertagging and another maximum entropy classifier is implemented with additional features for supertagging. Morphological features of words of an agglutinative language such as Turkish are used in order to improve the accuracy of POS tagging and supertagging processes. This indicates direct relationships between morphemes and lexical categories. The effects of using the improved supertagger are tested on dependency parsers by means of using supertags as rich parts of speech tags. Additionally, using POS taggers that assign multiple part of speech tags to the ambiguous words is suggested as another potential improvement for supertaggers.


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Type-raising is part of theory of Combinatory Categorial Grammar, by which all arguments including complements are type-raised. Generating type-raising rules in an automatic manner in the compile-time via a simple tool would make experimenting with Combinatory Categorial Grammar faster, allowing control on each run. In this study, created tool is tested with various grammars including large scale Eve database, giving results in O(N) where N is the number of verbs in the grammar.
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Citation Formats
B. K. Akkuş, “Supertagging with combinatory categorial grammar for dependency parsing,” M.S. - Master of Science, Middle East Technical University, 2014.