Sentence Simplification for Semantic Role Labeling

Proceedings of ACL-08: HLT, pages 344–352, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Sentence Simplification for Semantic Role Labeling David Vickrey and Daphne Koller Stanford University Stanford, CA 94305-9010 {dvickrey,koller}@cs.stanford.edu Abstract Parse-tree paths are commonly used to incor- porate 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 im- mense variety of syntactic expression. In this paper, we propose a general method for learn- ing 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 sen- tence. The model is parameterized by learned weights specifying preferences for some rules over others. After applying all possible trans- formations to a sentence, we are left with a set of candidate simplified sentences. We ap- ply our simplification system to semantic role labeling (SRL). As we do not have labeled ex- amples of correct simplifications, we use la- beled training data for the SRL task to jointly learn both the weights of the simplification model and of an SRL model, treating the sim- plification as a hidden variable. By extracting and labeling simplified sentences, this com- bined simplification/SRL system better gener- alizes across syntactic variation. It achieves a statistically significant 1.2% F1 measure in- crease 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 se- mantic 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 pri- marily on syntactic features in order to identify and NPS VP VP NP PP Tom wants S a to eat VP NP NP salad croutons with Tom: NP S(NP) VP VP SVP T croutons:NP1 VP PP(with) T salad: NP1 VP T Figure 1: Parse with path features for verb “eat”. classify roles. Features derived from a syntactic parse of the sentence have proven particularly useful (Gildea & Jurafsky, 2002). For example, the syntac- tic subject of “give” is nearly always the Giver. Path features allow systems to capture both general pat- terns, e.g., that the ARG0 of a sentence tends to be the subject of the sentence, and specific usage, e.g., that the ARG2 of “give” is often a post-verbal prepo- sitional phrase headed by “to”. An example sentence with extracted path features is shown in Figure 1. A major problem with this approach is that the path from an argument to the verb can be quite complicated. In the sentence “He expected to re- ceive a prize for winning,” the path from “win” to its ARG0, “he”, involves the verbs “expect” and “re- ceive” and the preposition “for.” The corresponding path through the parse tree likely occurs a relatively …

Content truncated to 500 words