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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.

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 contextfree 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.

We describe songs of the California Thrasher (Toxostoma redivivum), a territorial, monogamous species whose complex songs are composed of extended sequences of phonetically diverse phrases. We take a network approach, so that network nodes represent specific phrases, and links or transitions between nodes describe a subgroup structure that reveals the syntax of phrases within the songs. We found that individual birds have large and largely distinct repertoires, with limited phrase sharing between neighbours and repertoire similarity decaying between individuals with distance apart, decaying also over time within individuals. During song sequences, only a limited number of phrases (ca. 15–20) were found to be actually “in play” at any given time; these phrases can be grouped into themes within which transitions are much more common than among them, a feature contributing to a small-world structure. It appears that such “small-world themes” arise abruptly, while old themes are abandoned more gradually during extended song sequences; most individual thrashers switch among 3–4 themes over the course of several successive songs, and some small-world themes appear to have specific roles in starting or ending thrasher songs.

Human languages are diverse and so precise statements of common properties must abstract away from specifics of particular languages. We note several abstract and absolute ‘Type 1’ universals and present some new ones built on a notion of ‘structural invariant’ that applies equally to constituent structure and individual grammatical morphemes. Crucially, these universals may be realized in structurally diverse ways.

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.

Communication among adaptive agents can be framed as language acquisition and broken down into three problems; symbol grounding, language learning, and language evolution. We propose that this view clarifies many of the difficulties framing issues of collaboration and self-organization. Additionally, we demonstrate simple classification systems that can provide the first step in grounding real-world data and provide general schema for constructing other such systems. The first system classifies auditory input from frog calls and is presented as a model of grounding objects. The second system uses the minimum description length framework to distinguish patterns of robot movement as a model of grounding actions.

Four different kinds of grammars that can define crossing dependencies in human language are compared here: (i) context sensitive rewrite grammars with rules that depend on context, (ii) matching grammars with constraints that filter the generative structure of the language, (iii) copying grammars which can copy structures of unbounded size, and (iv) generating grammars in which crossing dependencies are generated from a finite lexical basis. Context sensitive rewrite grammars are syntactically, semantically and computationally unattractive. Generating grammars have a collection of nice properties that ensure they define only “mildly context sensitive” languages, and Joshi has proposed that human languages have those properties too. But for certain distinctive kinds of crossing dependencies in human languages, copying or matching analyses predominate. Some results relevant to the viability of mildly context sensitive analyses and some open questions are reviewed.

Linguists rely on intuitive conceptions of structure when comparing expressions and languages. In an algebraic presentation of a language, some natural notions of similarity can be rigorously defined (e.g. among elements of a language, equivalence w.r.t. isomorphisms of the language; and among languages, equivalence w.r.t. isomorphisms of symmetry groups), but it turns out that slightly more complex and nonstandard notions are needed to capture the kinds of comparisons linguists want to make. This paper identifies some of the important notions of structural similarity, with attention to similarity claims that are prominent in the current linguistic tradition of transformational grammar.

A simple kind of “minimalist” transformational grammar is defined to study the problem of learning a language in which pronounced constituents may have moved arbitrarily far from their original sites. In these grammars, all linguistic variation is lexical: constituent order is determined by lexical functional elements, and structure building operations are universal. Given universal constraints on the category system, these grammars can be identified in the limit from a positive text of derived structures, where these structures contain no features except the pronounced, phonetic elements. Identification from pronounced strings alone is shown to be impossible. In the light of this last negative result and related problems, rather than assuming that the learner somehow determines constituent structure from prosodic and semantic cues, an alternative approach to the learning problem is proposed.

This paper surveys prominent mathematical approaches to language learning, with an emphasis on the common fundamental assumptions of various approaches. All approaches adopt some restrictive assumption about the nature of relevant causal influences, with much ongoing work directed to the problem of discovery and justification of these assumptions., Le présent article passe en revue plusieurs approches mathématiques importantes de l’apprentissage des langues/ langages. Nous mettons en lumière certains postulats fondamentaux communs, notamment le fait que toutes ces approches admettent des restrictions sur les facteurs capables d’influencer l’apprentissage. On notera qu’une part importante des travaux actuellement en cours dans ce domaine est précisément consacrée à la découverte et la justification de ces restrictions., Stabler E. P. Mathematics of language learning. In: Histoire Épistémologie Langage, tome 31, fascicule 1, 2009. Mathématiques et langage. pp. 127-145.

Theories about proofs in other theories can be used not only to provide representations of completed proofs, but also to provide an elegant, declarative, and logically pure method for controlling deductions. This idea is used implicitly in a widening range of applications, and deserves explicit consideration. In this paper, a technique for generating a standard proof-representation-building metatheory for Horn-clause theories defined, its logical semantics is carefully considered, and the sense in which the technique is correct and complete is defined. Then we show how such metatheories can elegantly represent a wide range of problems. We focus on some problems which are naturally formulated in terms of overly general axioms together with conditions on proofs which block exactly the derivations of incorrect results: diagnosis, planning, and natural-language parsing. This surprising approach can yield representations that are succinct, feasible, and close to the most intuitive, informal statement of the problem. Methods for using such an approach efficiently with left-to-right theorem provers are described.

The development of theorem-based design methods is considered. Theorem-based design uses formal logic to create provably correct implementations. Past work has focused on using formal logic and post-hoc proof for design verification. Here, the focus is on hardware synthesis functions, called hardware metafunctions, which synthesize hardware in a provably correct manner. Designs produced using the metafunctions are correct-by-construction and are formally related to their specifications by simple substitution or rewriting of terms within the correctness theorem for each metafunction. Typically, the metafunctions are parametric and, once proven correct, validate an entire class of designs. Theorem-based design is practical when the metafunctions and their proofs of correctness are machine-executable. This is accomplished using appropriate declarative languages with a strong formal basis and by developing the proofs of correctness using automatic theorem provers. The functional language SCHEME is used along with the Higher Order Logic (HOL) proof checker. An introduction to the use of higher-order logic as a design along with the verification of an adder array metafunction for an array multiplier is presented. >

The Minimalist Grammars (MGs) proposed by Stabler(1997) have tree-shaped derivations (Harkema, 2001b; Michaelis, 2001a). As in categorial grammars, each lexical item is an association between a vocabulary element and complex of features, and so the ”yields” or ”fringes” of the derivation trees are sequences of these lexical items, and the string parts of these lexical items are reordered in the course of the derivation. This paper shows that while the derived string languages can be ambiguous and non-context-free, the set of yields of the derivation trees is always context-free and unambiguous. In fact, the derivation yield languages are strictly deterministic context-free languages, which implies that they are LR(0), and that the generation of derivation trees from a yield language string can be computed in linear time. This result suggests that the work of MG parsing consists essentially of guessing the lexical entries associated with words and empty categories.

There is a quantifier scope ambiguity in (1). In addition to the preferred normal scope reading paraphrased in (1ns), this sentence has the inverse scope reading paraphrased in (1is): (1) Some linguist speaks every language. (1ns) There is some linguist x such that x speaks every language (1is) For every language y, there is some linguist or other who speaks y Liu (1990) and others point out that certain objects, such as those with de-creasing denotations, do not allow an inverse scope reading, as in: (2) Some linguist speaks at most 2 languages. (2ns) Some linguist x is such that x speaks at most 2 languages (2is) There are at most 2 languages y such that some linguist or other speaks those 2 languages y (2is) is perfectly intelligible: it says that linguists speak at most 2 languages altogether. This does not seem to be available as an interpretation of (2). This is arguably not just a preference; sentence (2) just cannot be interpreted as (2is).

The ITP System for MUC3 is diagrammed in Figure 1. The three major modules handle different units of processing: the Message Handler processes a message unit; the ITP NLU Module processes a sentence and builds a Cognitive Model of the message; and the MUC3 Template Reasoning Module processes a segment of discourse.

Quine's (1969) proposal that the foundationalist programs in epistemology should be abandoned in favor of a scientific study of how we come to hold our theories about the world is still widely misunderstood. It does not eliminate the possibility of rational adjudication of scientific dispute, nor is it essentially tied to behaviorist approaches in psychology. On the contrary, recent work in psychology and philosophy of science can very naturally be seen as embodying the sort of program envisioned by Quine; now freed of behaviorist strictures, it clearly addresses issues that have been of interest in traditional epistemology. This view is defended with particular attention to Quine's concerns with translation and the related concerns with belief individuation which have inspired critics of recent cognitive psychology.

The performance of a connectionist learning system on a simple problem has been described by Hinton and is briefly reviewed here: a finite function is learned, and the system generalizes correctly from partial information by finding simple “features” of the environment. For comparison, a very similar problem is formulated in the Gold paradigm of discrete learning functions. Identification in the limit from positive text of a large class of functions including Hinton's is achievable with a trivial, conservative learning strategy. Using Valiant's approach, we place an arbitrary finite bound on function complexity and then we can guarantee text and resource efficiency relative to a probabilistic criterion of success. But the connectionist system generalizes. That is, it uses a non-conservative learning strategy. We define a simple, non-conservative strategy that also generalizes like the connectionist system, finding simple “features” of the environment.

Saul Kripke has proposed an argument to show that there is a serious problem with many computational accounts of physical systems and with functionalist theories in the philosophy of mind. The problem with computational accounts is roughly that they provide no noncircular way to maintain that any particular function with an infinite domain is realized by any physical system, and functionalism has the similar problem because of the character of the functional systems that are supposed to be realized by organisms. This paper shows that the standard account of what it is for a physical system to compute a function can avoid Kripke's criticisms without being reduced to circularity; a very minor and natural elaboration of the standard account suffices to save both functionalist theories and computational accounts generally.

An earlier investigation (Bejar, 1983) had argued that experts' judgment of item difficulty could perhaps be usefully supplemented with linguistic information about the sentence from which the item was derived. To investigate that idea, we analyzed items from the earlier study to determine their syntactic structure. Three potential independent variables were studied by themselves and in conjunction with subject-matter ratings. The analysis suggested that the combination of experts' judgments and syntactic information about the sentence on which the item was based collectively predicted difficulty better than either judgment or syntactic information alone. Moreover, the proportions of variance in item difficulty accounted for by the judgments and syntactic information was 31%.

International audience; The constraint language for lambda structures (CLLS) is a description language for lambda terms. CLLS provides parallelism constraints to talk about the tree structure of lambda terms, and lambda binding constraints to specify variable binding. Parallelism constraints alone have the same expressiveness as context unification. In this paper, we show that lambda binding constraints can also be expressed in context unification when permitting tree regular constraints.