Minimalist parsing ed. by Robert C. Berwick and Edward P. Stabler

Reviewed by: Minimalist parsing ed. by Robert C. Berwick and Edward P. Stabler Cristiano Chesi Minimalist parsing. Ed. by Robert C. Berwick and Edward P. Stabler. Oxford: Oxford University Press, 2019. Pp. 192. ISBN 9780198795094. $45. The recent popularity of very large corpora and the availability of sophisticated language modeling approaches (e.g. GPT-3; Brown et al. 2020) have revitalized interest in natural language processing (NLP), but have also led to the almost total eclipse of the structuralist approach to parsing and (more generally) of the considerable achievements obtained in formal language studies. In this context, the minimalist program (Chomsky 1995), which is discussed in this book and in which the editors, Robert C. Berwick and Edward P. Stabler, played a remarkable leading role,1 may sometimes be disregarded. Yet this enterprise deserves serious attention from the following audiences: (i) those who do not want to give up on explanatorily adequate models (in Chomsky’s 1965 sense; see also Chomsky 2020), (ii) those who still prefer explicit symbolic formalisms (HPSG, LFG, CCG, TAG, among others) to subsymbolic ones (e.g. recurrent neural networks with various levels of sophistication), and (iii) those who approach language studies from a purely statistical perspective (including machine learning and attention-based networks enthusiasts) that does not sufficiently account for certain intricacies of language. This collection includes an introduction and six contributions to a 2015 workshop held at MIT on ‘Parsing approaches under the minimalist umbrella’. The first three contributions focus on various aspects of minimalist formalization (Ch. 1 by Robert C. Berwick and Edward P. Stabler, Ch. 2 by Sandiway Fong and Jason Ginsburg, and Ch. 3 by Ginsburg and Fong). The book then turns to specific linguistic phenomena: parsing-prosody interaction (Ch. 4 by Kristine M. Yu) and ellipsis resolution (Ch. 5 by Gregory M. Kobele). The final two contributions discuss efficient (and cognitively plausible) parsing strategies (Ch. 6 by Tim Hunter) and propose an explicit ‘linking theory’ between minimalist parsing predictions and neurometabolic activity recorded during an ecological story-listening task (Ch. 7 by Jixing Li and John Hale). Ch. 1 (by Berwick and Stabler) is more than an introduction to the forthcoming contributions: it sets the stage for the minimalist enterprise by proposing a minimum common formal denominator for otherwise heterogeneous approaches. It also states the major research goals that this volume aims to address: first, evaluating the actual computational implications and the consistency/correctness of the (often) informally formulated linguistic intuitions under a coherent framework; and second, evaluating how performance evidence (from both the psycholinguistic and the neurolinguistic perspective) can fit with specific complexity metrics built on the proposed parsing models. It is clear that this collection is greatly indebted to the formalism of Stabler 2011, which is efficiently presented in this chapter. The introduction includes a specification of lexical items with their features: morphosyntactic categories and their selection/licensing requirements for ‘destructive’ feature-driven structure-building operations. For instance, from Stabler’s perspective, Merge(α, β) will be successful if and only if either α or β is such that one item selects the morphosyntactic categories of the other, in the end projecting over the one selected. Given the lexical items in 1, in which the double colon associates a specific phonetic realization of the lexical item (to the left) with an ordered set of features (after the double colon), we can define Merge as exemplified in 2.2 [End Page 852] (1) Praises :: =D =D V, Bo :: D, Cal :: D, which :: =N D -wh, student :: N, ε :: =V C, ε :: =V +wh C (2) i. Merge([praises =D =D V], [Bo D]) = [praises =D V [praises =D =D V Bo =D]] ii. Merge([Cal D], [praises =D V …]) = [praises V [Cal =D [praises = D V [praises = D =D V Bo =D]]]] iii. Merge([ε =V C], [praises V …]) = ε C [ε = V C [praises V … ]] By extending the feature set categories with +Y (licensors) and −Y (licensees), we can also define internal Merge, namely Move, as in 3.3 (3) Move(ε = V +wh C [praises = D V [Cal D] [praises =D =D V [which = N...