Context-sensitive Constraints Research Articles

Assessing Corpus Evidence for Formal and Psycholinguistic Constraints on Nonprojectivity

Abstract Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads’ depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints.

A Multi-level Refinement Approach for Structural Synthesis of Optimal Probabilistic Models

Probabilistic models play an important role in many fields such as distributed systems and simulations. Like non-probabilistic systems, they can be synthesized using classical refinement-based techniques, but they also require identifying the probability distributions to be used and their parameters. Since a fully automated and blind refinement is generally undecidable, many works tried to synthesize them by looking for the parameters of the distributions. Syntax-guided synthesizing approaches are more powerful, they try to synthesize models structurally by using context-free grammars. However, many problems arise like huge search space, the complexity of generated models, and the limitation of context-free grammars to define constraints over the structure. In this paper, we propose a multi-step refinement approach, based on meta-models, offering several abstraction levels to reduce the size of the search space. More specifically, each refinement step is divided into two stages in which the desired shape of models is first described by context-sensitive constraints. In the second stage, model templates are instantiated by using global optimization techniques. We use our approach to a synthesize a set of optimal probabilistic models and show that context-sensitive constraints coupled with the multi-level abilities of the approach make the synthesis task more effective.

Representing and Learning Grammars in Answer Set Programming

In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.

Context-sensitive constraints for access control of business processes

Workflow management systems (WfMS) are used to automate and facilitate business processes of an enterprise. To simplify the administration, it is a common practice in many WfMS solutions to allocate a role to perform each activity of the process and then assign one or more users to each role. Typically, access control for WfMS is role-based with a support of constraints on users and roles. However, merely using role and constraints concepts can hardly satisfy modern access control requirements of a contemporary enterprise. Permissions should not solely depend on common static and dynamic principles, but they must be influenced by the context in which the access is requested. In this paper, we focus on the definition and enforcement of the context-sensitive constraints for workflow systems. We extended the common role-based constraints listed in literature with context-sensitive information and workflow specific components. Also, we propose a mechanism for enforcing such constraints within WfMS.

Downward Causation: Polanyi and Prigogine

Downward Causation: Polanyi and Prigogine

Fortifying macros

AbstractExisting macro systems force programmers to make a choice between clarity of specification and robustness. If they choose clarity, they must forgo validating significant parts of the specification and thus produce low-quality language extensions. If they choose robustness, they must write in a style that mingles the implementation with the specification and therefore obscures the latter. This paper introduces a new language for writing macros. With the new macro system, programmers naturally write robust language extensions using easy-to-understand specifications. The system translates these specifications into validators that detect misuses—including violations of context-sensitive constraints—and automatically synthesize appropriate feedback, eliminating the need for ad hoc validation code.

Fortifying macros

Existing macro systems force programmers to make a choice between clarity of specification and robustness. If they choose clarity, they must forgo validating significant parts of the specification and thus produce low-quality language extensions. If they choose robustness, they must write in a style that mingles the implementation with the specification and therefore obscures the latter. This paper introduces a new language for writing macros. With the new macro system, programmers naturally write robust language extensions using easy-to-understand specifications. The system translates these specifications into validators that detect misuses - including violations of context-sensitive constraints - and automatically synthesize appropriate feedback, eliminating the need for ad hoc validation code.

The Self-Organization of Intentional Action

The Self-Organization of Intentional Action

DIAGRAM

An explanatory overview is given of DIAGRAM, a large and complex grammar used in an artificial intelligence system for interpreting English dialogue. DIAGRAM is an augmented phrase-structure grammar with rule procedures that allow phrases to inherit attributes from their constituents and to acquire attributes from the larger phrases in which they themselves are constituents. These attributes are used to set context-sensitive constraints on the acceptance of an analysis. Constraints can be imposed by conditions on dominance as well as by conditions on constituency. Rule procedures can also assign scores to an analysis to rate it as probable or unlikely. Less likely analyses can be ignored by the procedures that interpret the utterance. For every expression it analyzes, DIAGRAM provides an annotated description of the structure. The annotations supply important information for other parts of the system that interpret the expression in the context of a dialogue. Major design decisions are explained and illustrated. Some contrasts with transformational grammars are pointed out and problems that motivate a plan to use metarules in the future are discussed. (Metarules derive new rules from a set of base rules to achieve the kind of generality previously captured by transformational grammars but without having to perform transformations on syntactic analyses.)