Clinicians often use speech to characterize neurodegenerative disorders. Such characterizations require clinical judgment, which is subjective and can require extensive training. Quantitative Production Analysis (QPA) can be used to obtain objective quantifiable assessments of patient functioning. However, such human-based analyses of speech are costly and time consuming. Inexpensive off-the-shelf technologies such as speech recognition and part of speech taggers may avoid these problems. This study evaluates the ability of an automatic speech to text transcription system and a part of speech tagger to assist with measuring pronoun and verb ratios, measures based on QPA. Five participant groups provided spontaneous speech samples. One group consisted of healthy controls, while the remaining groups represented four subtypes of frontotemporal dementia. Findings indicated measurement of pronoun and verb ratio was robust despite errors introduced by automatic transcription and the tagger and despite these off-the-shelf products not having been trained on the language obtained from speech of the included population.

Recent psycholinguistic evidence suggests that human parsing of moved elements is ‘active’, and perhaps even ‘hyper-active’: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener. This paper presents a formal, sound and complete parser for Minimalist Grammars whose search space contains branching points that we can identify as the locus of the decision to perform this kind of active gap-finding. This brings formal models of parsing into closer contact with recent psycholinguistic theorizing than was previously possible.

Studies of parsing inspired by the Minimalist Program have various goals. They flesh out and unify proposals in syntax; they define properties of fundamental structure-building mechanisms; and they provide mechanisms for psycholinguistic models. In this brief overview, some historical antecedents are noted, and then foundational perspectives underpinning some recent work in this tradition are outlined, with associated basic results on locality and efficiency. Finally, a comparative guide to some notational differences is presented.

This book is the first dedicated to linguistic parsing—the processing of natural language according to the rules of a formal grammar—in the minimalist framework. While the Minimalist Program has been at the forefront of generative grammar for several decades, it often remains inaccessible to computer scientists and others in adjacent fields. In particular, minimalism reveals a surprising paradox: human language is simpler than we thought, and yet it cannot be processed by the machinery used by computer scientists. In this volume, experts in the field show how to resolve this apparent paradox, and how to turn Chomsky’s abstract theories into working computer programs that can process sentences or make predictions about the time course of brain activity when dealing with language. The book will appeal to graduate students and researchers in formal syntax, computational linguistics, psycholinguistics, and computer science.

This paper presents a left-corner parser for minimalist grammars. The relation between the parser and the grammar is transparent in the sense that there is a very simple 1-1 correspondence between derivations and parses. Like left-corner context-free parsers, left-corner minimalist parsers can be non-terminating when the grammar has empty left corners, so an easily computed left-corner oracle is defined to restrict the search.

Three different foundational ideas can be identified in recent syntactic theory: structure from substitution classes, structure from dependencies among heads, and structure as the result of optimizing preferences. As formulated in this review, it is easy to see that these three ideas are completely independent. Each has a different mathematical foundation, each suggests a different natural connection to meaning, and each implies something different about how language acquisition could work. Since they are all well supported by the evidence, these three ideas are found in various mixtures in the prominent syntactic traditions. From this perspective, if syntax springs fundamentally from a single basic human ability, it is an ability that exploits a coincidence of a number of very different things.

While it has long been clear that prosody should be part of the grammar influencing the action of the syntactic parser, how to bring prosody into computational models of syntactic parsing has remained unclear. The challenge is that prosodic information in the speech signal is the result of the interaction of a multitude of conditioning factors. From this output, how can we factor out the contribution of syntax to conditioning prosodic events? And if we are able to do that factorization and define a production model from the syntactic grammar to a prosodified utterance, how can we then define a comprehension model based on that production model? In this case study of the Samoan morphosyntax-prosody interface, we show how to factor out the influence of syntax on prosody in empirical work and confirm there is invariable morphosyntactic conditioning of high edge tones. Then, we show how this invariability can be precisely characterized and used by a parsing model that factors the various influences of morphosyntax on tonal events. We expect that models of these kinds can be extended to more comprehensive perspectives on Samoan and to languages where the syntax/prosody coupling is more complex.

The goal of this paper is to give a precise, formal account of certain fundamental notions in minimalist syntax. Particular attention is given to the comparison of token-based (multidominance) and chain-based perspectives on Merge. After considering a version of Transfer that violates the No-Tampering Condition (NTC), we sketch an alternative, NTC-compliant version.

Neurolinguistic accounts of sentence comprehension identify a network of relevant brain regions, but do not detail the information flowing through them. We investigate syntactic information. Does brain activity implicate a computation over hierarchical grammars or does it simply reflect linear order, as in a Markov chain? To address this question, we quantify the cognitive states implied by alternative parsing models. We compare processing-complexity predictions from these states against fMRI timecourses from regions that have been implicated in sentence comprehension. We find that hierarchical grammars independently predict timecourses from left anterior and posterior temporal lobe. Markov models are predictive in these regions and across a broader network that includes the inferior frontal gyrus. These results suggest that while linear effects are wide-spread across the language network, certain areas in the left temporal lobe deal with abstract, hierarchical syntactic representations.

Recent computational, mathematical work on learnability extends to classes of languages that plausibly include the human languages, but there is nevertheless a gulf between this work and linguistic theory. The languages of the two elds seem almost completely disjoint and incommensurable. This paper shows that this has happened, at least in part, because the recent advances in learnability have been misdescribed in two important respects. First, they have been described as resting on ‘empiricist’ conceptions of language, when actually, in fundamental respects that are made precise here, they are equally compatible with the ‘rationalist’, ‘nativist’ traditions in linguistic theory. Second, the recent mathematical proposals have sometimes been presented as if they not only advance but complete the account of human language acquisition, taking the rather dramatic dierence between what current mathematical models can achieve and what current linguistic theories tell us as an indication that current linguistic theories are quite generally mistaken. This paper compares the two perspectives and takes some rst steps toward a unied theory, aiming

The deeper, more recursive structures posited by linguists reflect important insights into similarities among linguistic constituents and operations, which would seem to be lost in computational models that posit simpler, flatter structures. We show how this apparent conflict can be resolved with a substantive theory of how linguistic computations are implemented. We begin with a review of standard notions of recursive depth and some basic ideas about how computations can be implemented. We then articulate a consensus position about linguistic structure, which disentangles what is computed from how it is computed. This leads to a unifying perspective on what it is to represent and manipulate structure, within some large classes of parsing models that compute exactly the consensus structures in such a way that the depth of the linguistic analysis does not correspond to processing depth. From this unifying perspective, we argue that adequate performance models must, unsurprisingly, be more superficial than adequate linguistic models, and that the two perspectives are reconciled by a substantial theory of how linguistic computations are implemented. In a sense that will be made precise, the recursive depth of a structural analysis does not correspond in any simple way to depth of the calculation of that structure in linguistic performance.

Bever calls grammar “the epicenter of all language behavior” but observes that the relation between grammar and models of linguistic behavior remains unclear. This chapter reviews some major advances in our understanding of common features of diverse grammar formalisms. Stabler argues that mild context-sensitive grammars can both define the sentences of human languages (weak adequacy) and also provide the structures of those languages (strong adequacy). He also points out that different formalisms (context-free grammars (CFG), tree-adjoining grammars (TAG), combinatory categorial grammars (CCG), set-local multicomponent grammars (MCTAG), abstract categorial grammars (ACG2,4), multiple context-free grammars (MCFG), Minimalist grammars (MG), and context-sensitive grammars (CSG)) are weakly equivalent in the sense that they define exactly the same sets of sentences. These grammars are expressive enough to define the discontinuous dependencies of human languages. Furthermore, computational methods provide tools for describing rather abstract similarities of structures and languages.

Minimalist grammars (MGs) and multiple context-free grammars (MCFGs) are weakly equivalent in the sense that they define the same languages, a large mildly context-sensitive class that properly includes context-free languages. But in addition, for each MG, there is an MCFG which is strongly equivalent in the sense that it defines the same language with isomorphic derivations. However, the structure-building rules of MGs but not MCFGs are defined in a way that generalizes across categories. Consequently, MGs can be exponentially more succinct than their MCFG equivalents, and this difference shows in parsing models too. An incremental, top-down beam parser for MGs is defined here, sound and complete for all MGs, and hence also capable of parsing all MCFG languages. But since the parser represents its grammar transparently, the relative succinctness of MGs is again evident. Although the determinants of MG structure are narrowly and discretely defined, probabilistic influences from a much broader domain can influence even the earliest analytic steps, allowing frequency and context effects to come early and from almost anywhere, as expected in incremental models.

OF THE THESIS Dynamic binding of split antecedents

While research in ‘principles and parameters’ tradition [18] can be regarded as attributing as much as possible to universal grammar (UG) in order to understand how language acquisition is possible, Chomsky characterizes the ‘minimalist program’ as an effort to attribute as little as possible to UG while still accounting for the apparent diversity of human languages [23, p.4]. Of course, these two research strategies aim to be compatible, and ultimately should converge. Several of Chomsky’s own early contributions to the minimalist program have been fundamental and simple enough to allow easy mathematical and computational study. Among these contributions are (1) the characterization of ‘bare phrase structure,’ and (2) the definition of a structure building operation merge which applies freely to lexical material, with constraints that ‘filter’ the results only at the PF and LF interfaces. The first studies inspired by (1) and (2) are ‘stripped down’ to such a degree that they may seem unrelated to minimalist proposals, but in this paper, we show how some easy steps begin to bridge the gap. This paper briefly surveys some proposals about (3) syntactic features that license structure building, (4) ‘locality,’ the domain over which structure building functions operate, (5) ‘linearization’, determining (in part) the order of pronounced forms, and (6) the proposal that merge sometimes involves ‘copies.’ Two very surprising, overarching results emerge. First, seemingly diverse proposals are revealed to be remarkably similar, often defining identical languages, with recursive mechanisms that are similar (Thms. 25, below). As noted by Joshi [47] and others, this remarkable convergence extends across linguistic traditions and even to mathematical work that started with very different assumptions and goals (Thm. 1, below). Second, all the mechanisms reviewed here define sets of structures with nice computational properties; they all define ‘abstract families of languages’ (AFLs) that are efficiently recognizable. This raises an old puzzle: why would human languages have properties that guarantee the existence of parsing methods that correctly and efficiently identify all and only the well-formed sentences, when humans apparently do not use methods of that kind? A speculation – perhaps supported by the whole cluster of AFL properties – is that this

This chapter reports on research showing that it may be a universal structural property of human languages that they fall into a class of languages defined by mildly context-sensitive grammars. It also investigates the issue of whether there are properties of language that are needed to guarantee that it is learnable. It suggests that languages are learnable if they have a finite Vapnik-Chervonenkis (VC) dimension (where the VC dimension provides a combinatorial measure of complexity for a set of languages). Informally, a finite VC dimension requires that there be restrictions on the set of languages to be learned such that they do not differ from one another in arbitrary ways. These restrictions can be construed as universals that are required for language to be learnable (given formal language learnability theory). The chapter concludes by pointing out that formalizations of the semantic contribution (e.g., compositionality) to language learning might yield further insight into language universals.

Language evolution is a primary problem in designing collaborative agents from a linguistic perspective. The problem of language evolution involves determining how the languages used by a collection of agents may change over time, and whether an initially homogeneous language will stay uniformly understandable or will diverge into mutually unintelligible dialects. In simple regimes, it has been possible to determine properties like the maximum error rate a communication channel can withstand before the agents lose their ability to communicate in a coherent language. This study shows that the evolutionary dynamics of linguistic convergence is affected not only by learning fidelity, but also topology of the population network, language structure, and grammar networks. These factors are just a few that contribute to understanding of complex language convergence dynamics. Four canonical types of graphs with substantially different structural properties were considered in this experiment: the complete graph, a regular ring lattice, a random graph, and a small-world graph. Less-than-complete graphs make sense from a biological or sociological standpoint since learning a language that is similar to preexisting ones would seem easier than learning a drastically different language. For each graph we explored, the dynamics were different. Not only did different networks exhibit different patterns of language convergence, but also reached an equilibrium state at different rates. In technological applications where agents learn from each other, where it is desirable for the overall system to converge, these results may provide some guidance for designing properties of the language or state representation, depending on the degree of convergence desired. c © 2001 John Wiley & Sons, Ltd Cooperative Control of Distributed Multi-Agent Systems Jeff S. Shamma, ed. c © XXXX John Wiley & Sons, Ltd 352 COHESION OF LANGUAGES IN GRAMMAR NETWORKS

Language evolution is a primary problem in designing collaborative agents from a linguistic perspective. The problem of language evolution involves determining how the languages used by a collection of agents may change over time, and whether an initially homogeneous language will stay uniformly understandable or will diverge into mutually unintelligible dialects. In simple regimes, it has been possible to determine properties like the maximum error rate a communication channel can withstand before the agents lose their ability to communicate in a coherent language. This study shows that the evolutionary dynamics of linguistic convergence is affected not only by learning fidelity, but also topology of the population network, language structure, and grammar networks. These factors are just a few that contribute to understanding of complex language convergence dynamics. Four canonical types of graphs with substantially different structural properties were considered in this experiment: the complete graph, a regular ring lattice, a random graph, and a small-world graph. Less-than-complete graphs make sense from a biological or sociological standpoint since learning a language that is similar to preexisting ones would seem easier than learning a drastically different language. For each graph we explored, the dynamics were different. Not only did different networks exhibit different patterns of language convergence, but also reached an equilibrium state at different rates. In technological applications where agents learn from each other, where it is desirable for the overall system to converge, these results may provide some guidance for designing properties of the language or state representation, depending on the degree of convergence desired. c © 2001 John Wiley & Sons, Ltd Cooperative Control of Distributed Multi-Agent Systems Jeff S. Shamma, ed. c © XXXX John Wiley & Sons, Ltd 352 COHESION OF LANGUAGES IN GRAMMAR NETWORKS