Better Binarization for the CKY Parsing

We present a study on how grammar binarization empirically affects the efficiency of the CKY parsing. We argue that binarizations affect parsing efficiency primarily by affecting the number of incomplete constituents generated, and the effectiveness of binarization also depends on the nature of the input. We propose a novel binarization method utilizing rich information learnt from training corpus. Experimental results not only show that different binarizations have great impacts on parsing efficiency, but also confirm that our learnt binarization outperforms other existing methods. Furthermore we show that it is feasible to combine existing parsing speed-up techniques with our binarization to achieve even better performance.

• Type: Conference
• Year: 2008
• Where: EMNLP
• Authors: Xinying Song, Shilin Ding, Chin-Yew Lin
• Keywords: CKY Parsing, EMNLP 2008, Natural Language Processing, Parsing, Rich Information Learnt