This paper investigates perceptron training for a wide-coverage CCG parser and compares the perceptron with a log-linear model. The CCG parser uses a phrase-structure parsing model and dynamic programming in the form of the Viterbi algorithm to find the highest scoring derivation. The difficulty in using the perceptron for a phrase-structure parsing model is the need for an efficient decoder. We exploit the lexicalized nature of CCG by using a finite-state supertagger to do much of the parsing work, resulting in a highly efficient decoder. The perceptron performs as well as the log-linear model; it trains in a few hours on a single machine; and it requires only a few hundred MB of RAM for practical training compared to 20 GB for the log-linear model. We also investigate the order in which the training examples are presented to the online perceptron learner, and find that order does not significantly affect the results.
They REF ) also used the perceptron algorithm to train a CCG parser.
Perceptron Training for a Wide-Coverage Lexicalized-Grammar Parser
Workshop on Deep Linguistic Processing
Computer Science