文章基本信息

标题：Incremental, Predictive Parsing with Psycholinguistically Motivated Tree-Adjoining Grammar
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作者：Vera Demberg ; Frank Keller ; Alexander Koller 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2013
卷号：39
期号：4
页码：1025-1066
DOI：10.1162/COLI_a_00160
语种：English
出版社：MIT Press
摘要：Psycholinguistic research shows that key properties of the human sentence processor are incrementality, connectedness (partial structures contain no unattached nodes), and prediction (upcoming syntactic structure is anticipated). There is currently no broad-coverage parsing model with these properties, however. In this article, we present the first broad-coverage probabilistic parser for PLTAG, a variant of TAG that supports all three requirements. We train our parser on a TAG-transformed version of the Penn Treebank and show that it achieves performance comparable to existing TAG parsers that are incremental but not predictive. We also use our PLTAG model to predict human reading times, demonstrating a better fit on the Dundee eye-tracking corpus than a standard surprisal model.