Abstract

This paper presents an empirical assessment of the LFG-DOP model introduced by Bod & Kaplan (1998). The parser we describe uses fragments from LFG-annotated sentences to parse new sentences and Monte Carlo techniques to compute the most probable parse. While our main goal is to test Bod & Kaplan's model, we will also test a version of LFG-DOP which treats generalized fragments as previously unseen events. Experiments with the Verbmobil and Homecentre corpora show that our version of LFG-DOP outperforms Bod & Kaplan's model, and that LFG's functional information improves the parse accuracy of tree structures. 1

Citations

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