Data and models for statistical parsing with combinatory categorial grammar
This dissertation is concerned with the creation of training data and the development of probability models for statistical parsing of English with Combinatory Categorial Grammar (CCG). Parsing, or syntactic analysis, is a prerequisite for semantic interpretation, and forms therefore an integral part of any system which requires natural language understanding. Since almost all naturally occurring sentences are ambiguous, it is not sufficient (and often impossible) to generate all possible syntactic analyses. Instead, the parser needs to rank competing analyses and select only the most likely ones. A statistical parser uses a probability model to perform this task. I propose a number of ways in which such probability models can be defined for CCG. The kinds of models developed in this dissertation, generative models over normal-form derivation trees, are particularly simple, and have the further property of restricting the set of syntactic analyses to those corresponding to a canonical derivation structure. This is important to guarantee that parsing can be done efficiently. In order to achieve high parsing accuracy, a large corpus of annotated data is required to estimate the parameters of the probability
Hockenmaier, Julia
Data and models for statistical parsing with combinatory categorial grammar. This dissertation is concerned with the creation of training data and the development of probability models for statistical parsing of English with Combinatory Categorial Grammar (CCG). Parsing, or syntactic analysis, is a prerequisite for semantic interpretation, and forms therefore an integral part of any system which requires natural language understanding. Since almost all naturally occurring sentences are ambiguous, it is not sufficient (and often impossible) to generate all possible syntactic analyses. Instead, the parser needs to rank competing analyses and select only the most likely ones. A statistical parser uses a probability model to perform this task. I propose a number of ways in which such probability models can be defined for CCG. The kinds of models developed in this dissertation, generative models over normal-form derivation trees, are particularly simple, and have the further property of restricting the set of syntactic analyses to those corresponding to a canonical derivation structure. This is important to guarantee that parsing can be done efficiently. In order to achieve high parsing accuracy, a large corpus of annotated data is required to estimate the parameters of the probability
data model statistical parse combinatory_categorial grammar concerned creation training_data development probability model statistical parse english combinatory_categorial grammar ccg parsing syntactic_analysis prerequisite semantic_interpretation form therefore integral_part system_require natural_language understanding since almost naturally_occur sentence ambiguous sufficient often impossible generate possible syntactic_analysis instead parser need rank compete analysis_select likely statistical parser use probability model perform_task propose number way probability model define ccg kind model_develop generative model normal form derivation tree particularly simple property set syntactic_analysis correspond canonical derivation structure important guarantee parse efficiently order_achieve high parsing accuracy large_corpus annotate data require estimate parameter probability