Empirical Risk Minimization with Approximations of Probabilistic Grammars

Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. We present a framework, reminiscent of structural risk minimization, for empirical risk minimization of the parameters of a fixed probabilistic grammar using the log-loss. We derive sample complexity bounds in this framework that apply both to the supervised setting and the unsupervised setting.

Natural Language Syntax Complies with the Free-Energy Principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design - such as "minimal search" criteria from theoretical syntax - adhere to the FEP. This affords a greater degree of explanatory power to the FEP - with respect to higher language functions - and offers linguistics a grounding in first principles with respect to computability. We show how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference

Phrase structure grammars as indicative of uniquely human thoughts

I argue that the ability to compute phrase structure grammars is indicative of a particular kind of thought. This type of thought that is only available to cognitive systems that have access to the computations that allow the generation and interpretation of the structural descriptions of phrase structure grammars. The study of phrase structure grammars, and formal language theory in general, is thus indispensable to studies of human cognition, for it makes explicit both the unique type of human thought and the underlying mechanisms in virtue of which this thought is made possible

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

On the learning of vague languages for syntactic pattern recognition

The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition

Toward a balanced grammatical description

The writer of a grammatical description attempts to accomplish many goals in one complex document. Some of these goals seem to conflict with one another, thus causing tension, discouragement and paralysis for many descriptive linguists. For example, all grammar writers want their work to speak clearly to general linguists and to specialists in their language area tradition. Yet a grammar that addresses universal issues, may not be detailed enough for specialists; while a highly detailed description written in a specialized areal framework may be incomprehensible to those outside of a particular tradition. In the present chapter, I describe four tensions that grammar writers often face, and provide concrete suggestions on how to balance these tensions effectively and creatively. These tensions are: • Comprehensiveness vs. usefulness. • Technical accuracy vs. understandability. • Universality vs. specificity. • A ‘form-driven’ vs. a ‘function-driven’ approach. By drawing attention to these potential conflicts, I hope to help free junior linguists from the unrealistic expectation that their work must fully accomplish all of the ideals that motivate the complex task of describing the grammar of a language. The goal of a description grammar is to produce an esthetically pleasing, intellectually stimulating, and genuinely informative piece of work.National Foreign Language Resource Cente

Analysis by Synthesis: A (Re-)Emerging Program of Research for Language and Vision

This contribution reviews (some of) the history of analysis by synthesis, an approach to perception and comprehension articulated in the 1950s. Whereas much research has focused on bottom-up, feed-forward, inductive mechanisms, analysis by synthesis as a heuristic model emphasizes a balance of bottom-up and knowledge-driven, top-down, predictive steps in speech perception and language comprehension. This idea aligns well with contemporary Bayesian approaches to perception (in language and other domains), which are illustrated with examples from different aspects of perception and comprehension. Results from psycholinguistics, the cognitive neuroscience of language, and visual object recognition suggest that analysis by synthesis can provide a productive way of structuring biolinguistic research. Current evidence suggests that such a model is theoretically well motivated, biologically sensible, and becomes computationally tractable borrowing from Bayesian formalizations