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  • Multiple movement dependencies and parasitic gaps
  • Isaac Gould

1. Introduction

Nissenbaum (2000) and Heck and Himmelreich (2017) (henceforth HH) are two prominent works that are noteworthy for focusing on the interaction of multiple movement dependencies (MMDs) and parasitic gaps (PGs).1 However, these two works consider disjoint and contrasting types of data paradigms. In this squib, I take both types of paradigms into consideration. In doing so, there are three inter-related questions that arise for our understanding of PGs (and in particular for PGs that involve certain MMDs). The first question asks what the correct descriptive generalizations about PG paradigms are. Going beyond such a generalization, we can also ask how to properly account for the data (and whether a unified analysis is possible). Finally, and more specifically, we can ask how to account for the compositional semantics of PGs, especially those involving MMDs.

Nissenbaum presents a generalization based on English data regarding (a) the PGs in a PG-containing vP-adjunct, and (b) the XPs that move to the edge of a vP immediately dominating such an adjunct, crucially attaching above the adjunct. For convenience, I will refer to these XPs as edge-XPs. As we will see, Nissenbaum's generalization is that in these specific circumstances, each edge-XP must associate with a PG in the adjunct (i.e., be interpreted as the filler for the gap of a PG). However, HH introduce a data paradigm from German that challenges this generalization.2 I also present a tweaked version of the generalization based on Nissenbaum's analysis that is again challenged by the data. In particular, we will see the possibility of multiple edge-XPs where only one of them associates [End Page 110] with a PG. This disparity raises the question of what the correct descriptive generalization of the PG paradigms should be.

When we consider the theoretical proposals of Nissenbaum and HH in sections 2 and 3, we will see that neither can account for the full set of attested data. Nissenbaum cannot fully account for the German data, and conversely, HH cannot fully account for Nissenbaum's data. We will see that it is not clear how to properly account for the data, and whether a unified analysis of the data is possible.

Finally, there is the related question of what the compositional semantics of the PG construction is. In contrast to HH, Nissenbaum focuses on the semantics of PGs, and indeed, he attempts to derive the generalization he proposes in a principled way via the nature of semantic composition. We will see that within the framework of these proposals, Nissenbaum's semantic treatment of the construction is not able to account for Heck and Himmelreich's data. We are then left with the broad question of how we can account for the compositional semantics of PGs, especially those involving MMDs.

Before continuing, I note two things in relation to the literature on German. First, PG data involving putative MMDs has appeared earlier in the literature (e.g., Fanselow 1993: 34, Müller 1995: 261–264, and Kathol 2001: 329). However, HH are noteworthy in developing a data set that allows for a clearer focus on the interaction of multiple A'-dependencies. HH's data, as far as I can tell, help us to see a potential confound (see section 3 and note 8) in the data that have been presented in all previous works when it comes to considering the application of Nissenbaum's generalization to German. In particular, HH's data set points us toward supplemental Heck and Himmelreich data (p.c.) that do not suffer from this potential confound. One reason to therefore focus on the current paradigm is its more comprehensive scope regarding A'-dependencies and PGs. Their proposal also distinguishes itself from what is found in these earlier works in having a detailed and restrictive theory for PGs involving MMDs (see Fanselow 1993: 35 for a sketch of a proposal). Looking at the various HH examples allows us to see clearly how their proposal works (and I note that applying HH's proposal to earlier German data is less than straightforward because of differences in reported...


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