tailieunhanh - Báo cáo khoa học: "Sentence Simplification for Semantic Role Labeling"

Parse-tree paths are commonly used to incorporate information from syntactic parses into NLP systems. These systems typically treat the paths as atomic (or nearly atomic) features; these features are quite sparse due to the immense variety of syntactic expression. In this paper, we propose a general method for learning how to iteratively simplify a sentence, thus decomposing complicated syntax into small, easy-to-process pieces. Our method applies a series of hand-written transformation rules corresponding to basic syntactic patterns — for example, one rule “depassivizes” a sentence. . | Sentence Simplification for Semantic Role Labeling David Vickrey and Daphne Koller Stanford University Stanford CA 94305-9010 dvickrey koller @ Abstract Parse-tree paths are commonly used to incorporate information from syntactic parses into NLP systems. These systems typically treat the paths as atomic or nearly atomic features these features are quite sparse due to the immense variety of syntactic expression. In this paper we propose a general method for learning how to iteratively simplify a sentence thus decomposing complicated syntax into small easy-to-process pieces. Our method applies a series of hand-written transformation rules corresponding to basic syntactic patterns for example one rule depassivizes a sentence. The model is parameterized by learned weights specifying preferences for some rules over others. After applying all possible transformations to a sentence we are left with a set of candidate simplified sentences. We apply our simplification system to semantic role labeling SRL . As we do not have labeled examples of correct simplifications we use labeled training data for the SRL task to jointly learn both the weights of the simplification model and of an SRL model treating the simplification as a hidden variable. By extracting and labeling simplified sentences this combined simplification SRL system better generalizes across syntactic variation. It achieves a statistically significant F1 measure increase over a strong baseline on the Conll-2005 SRL task attaining near-state-of-the-art performance. 1 Introduction In semantic role labeling SRL given a sentence containing a target verb we want to label the semantic arguments or roles of that verb. For the verb eat a correct labeling of Tom ate a salad is ARG0 Eater Tom ARG1 Food salad . Current semantic role labeling systems rely primarily on syntactic features in order to identify and S Tom NP S NP VP S VP VP T NP VP salad NP1 VP T Tom wants S croutons PP with NPl VP T VP NP NP