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.
There also exists a long line of work on post-processing surface-syntax constituency trees to recover traces in the PTB @cite_35 @cite_13 @cite_28 @cite_33 , pre-processing sentences such that they contain tokens for traces before parsing @cite_10 , or directly parsing sentences to either PTB-style trees with empty elements or pre-processed trees that can be deterministically converted to PTB-style trees @cite_15 @cite_30 @cite_21 @cite_38 @cite_2 @cite_36 @cite_3 . However, all of these works are primarily concerned with recovering traces for phenomena such as Wh-movement or control and raising constructions and, with the exception of , none of these works attempt to output the co-indexing that is used for analyzing gapping constructions. And again, none of these works try to reconstruct elided material.
@cite_30 @cite_35 @cite_38 @cite_33 @cite_28 @cite_10 @cite_36 @cite_21 @cite_3 @cite_2 @cite_15 @cite_13
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This paper explores the problem of finding non-local dependencies. First, we isolate a set of features useful for this task. Second, we develop both a two-step approach which combines a trace tagger with a state-of-the-art lexicalized parser and a one-step approach which finds nonlocal dependencies while parsing. We find that the former outperforms the latter because it makes better use of the features we isolate. This paper describes a simple pattern-matching algorithm for recovering empty nodes and identifying their co-indexed antecedents in phrase structure trees that do not contain this information. The patterns are minimal connected tree fragments containing an empty node and all other nodes co-indexed with it. This paper also proposes an evaluation procedure for empty node recovery procedures which is independent of most of the details of phrase structure, which makes it possible to compare the performance of empty node recovery on parser output with the empty node annotations in a gold-standard corpus. Evaluating the algorithm on the output of Charniak's parser (Charniak, 2000) and the Penn treebank (, 1993) shows that the pattern-matching algorithm does surprisingly well on the most frequently occuring types of empty nodes given its simplicity. We present a simple, language-independent method for integrating recovery of empty elements into syntactic parsing. This method outperforms the best published method we are aware of on English and a recently published method on Chinese. We present a two stage parser that recovers Penn Treebank style syntactic analyses of new sentences including skelet al syntactic structure, and, for the first time, both function tags and empty categories. The accuracy of the first-stage parser on the standard Parseval metric matches that of the (Collins, 2003) parser on which it is based, despite the data fragmentation caused by the greatly enriched space of possible node labels. This first stage simultaneously achieves near state-of-the-art performance on recovering function tags with minimal modifications to the underlying parser, modifying less than ten lines of code. The second stage achieves state-of-the-art performance on the recovery of empty categories by combining a linguistically-informed architecture and a rich feature set with the power of modern machine learning methods. We present a novel approach for finding discontinuities that outperforms previously published results on this task. Rather than using a deeper grammar formalism, our system combines a simple unlexicalized PCFG parser with a shallow pre-processor. This pre-processor, which we call a trace tagger, does surprisingly well on detecting where discontinuities can occur without using phase structure information. This paper proposes a left-corner parser which can identify nonlocal dependencies. Our parser integrates nonlocal dependency identification into a transition-based system. We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies. An experimental result demonstrates that our parser achieves a good balance between constituent parsing and nonlocal dependency identification. This paper describes a parser which generates parse trees with empty elements in which traces and fillers are co-indexed. The parser is an unlexicalized PCFG parser which is guaranteed to return the most probable parse. The grammar is extracted from a version of the PENN treebank which was automatically annotated with features in the style of Klein and Manning (2003). The annotation includes GPSG-style slash features which link traces and fillers, and other features which improve the general parsing accuracy. In an evaluation on the PENN treebank (, 1993), the parser outperformed other unlexicalized PCFG parsers in terms of labeled bracketing f-score. Its results for the empty category prediction task and the trace-filler co-indexation task exceed all previously reported results with 84.1 and 77.4 f-score, respectively. General treebank analyses are graph structured, but parsers are typically restricted to tree structures for efficiency and modeling reasons. We propose a new representation and algorithm for a class of graph structures that is flexible enough to cover almost all treebank structures, while still admitting efficient learning and inference. In particular, we consider directed, acyclic, one-endpoint-crossing graph structures, which cover most long-distance dislocation, shared argumentation, and similar tree-violating linguistic phenomena. We describe how to convert phrase structure parses, including traces, to our new representation in a reversible manner. Our dynamic program uniquely decomposes structures, is sound and complete, and covers 97.3 of the Penn English Treebank. We also implement a proof-of-concept parser that recovers a range of null elements and trace types. This paper presents a spinal parsing algorithm that can jointly detect empty elements. This method achieves state-of-theart performance on English and Japanese empty element recovery problems. In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1 87.5 constituent precision recall, an average improvement of 2.3 over (Collins 96).