Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments. Surface syntax representations of such sentences are often produced poorly by parsers, and even if correct, not well suited to downstream natural language understanding tasks such as relation extraction that are typically designed to extract information from sentences with canonical clause structure. In this paper, we present two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges. We find that both methods can reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap. We further demonstrate that one of our methods can be applied to other languages based on a case study on Swedish.
Lastly, several methods have been proposed for resolving other forms of ellipsis, including VP ellipsis @cite_19 @cite_14 @cite_37 @cite_24 and sluicing @cite_27 but none of these methods consider gapping constructions.
@cite_37 @cite_14 @cite_24 @cite_19 @cite_27
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I compare several types of knowledge-based and knowledge-poor approaches to anaphora and ellipsis resolution. The former are able to capture fine-grained distinctions that depend on lexical meaning and real world knowledge, but they are generally not robust. The latter show considerable promise for yielding wide coverage systems. However, they consistently miss a small but significant subset of cases that are not accessible to rough-grained techniques of intepretation. I propose a sequenced model which first applies the most computationally efficient and inexpensive methods to resolution and then progresses successively to more costly techniques to deal with cases not handled by previous modules. Confidence measures evaluate the judgements of each component in order to determine which instances of anaphora or ellipsis are to be passed on to the next, more fine-grained subsystem.1 This paper describes a Verb Phrase Ellipsis (VPE) detection system, built for robustness, accuracy and domain independence. The system is corpus-based, and uses a variety of machine learning techniques on free text that has been automatically parsed using two different parsers. Tested on a mixed corpus comprising a range of genres, the system achieves a 72 F1-score. It is designed as the first stage of a complete VPE resolution system that is input free text, detects VPEs, and proceeds to find the antecedents and resolve them. Verb phrase (VP) ellipsis is the omission of a verb phrase whose mean- ing can be reconstructed from the linguistic or real-world context. It is licensed in English by auxiliary verbs, often modal auxiliaries: She can go to Hawaii but he can’t [e] . This paper describes a system called ViPER ( VP E llipsis R esolver) that detects and resolves VP ellipsis, relying on linguistic principles such as syntactic parallelism, modality correlations, and the delineation of core vs. peripheral sentence con- stituents. The key insight guiding the work is that not all cases of el- lipsis are equally difficult: some can be detected and resolved with high confidence even before we are able to build systems with human-level semantic and pragmatic understanding of text. This paper reports on an empirically based system that automatically resolves VP ellipsis in the 644 examples identified in the parsed Penn Treebank. The results reported here represent the first systematic corpus-based study of VP ellipsis resolution, and the performance of the system is comparable to the best existing systems for pronoun resolution. The methodology and utilities described can be applied to other discourse-processing problems, such as other forms of ellipsis and anaphora resolution.The system determines potential antecedents for ellipsis by applying syntactic constraints, and these antecedents are ranked by combining structural and discourse preference factors such as recency, clausal relations, and parallelism. The system is evaluated by comparing its output to the choices of human coders. The system achieves a success rate of 94.8 , where success is defined as sharing of a head between the system choice and the coder choice, while a baseline recency-based scheme achieves a success rate of 75.0 by this measure. Other criteria for success are also examined. When success is defined as an exact, word-for-word match with the coder choice, the system performs with 76.0 accuracy, and the baseline approach achieves only 14.6 accuracy. Analysis of the individual components of the system shows that each of the structural and discourse constraints used are strong predictors of the antecedent of VP ellipsis.