Inference approach to grammar construction

A system of computer assisted grammar construction (CAGC) is presented in this paper. The CAGC system is designed to generate broad-coverage grammars for large natural language corpora by utilizing both an extended inside-outside algorithm and an automatic phrase bracketing (AUTO) system which is designed to provide the extended algorithm with constituent information during learning. This paper demonstrates the capability of the CAGC system to deal with realistic natural language problems and the usefulness of the AUTO system for constraining the inside-outside based grammar re-estimation. Performance results, including coverage, recall and precision, are presented for a grammar constructed for the Wall Street Journal (WSJ) corpus using the Penn Treebank.

Computational Structure of GPSG Models: Revised Generalized Phrase Structure Grammar

The primary goal of this report is to demonstrate how considerations from computational complexity theory can inform grammatical theorizing. To this end, generalized phrase structure grammar (GPSG) linguistic theory is revised so that its power more closely matches the limited ability of an ideal speaker--hearer: GPSG Recognition is EXP-POLY time hard, while Revised GPSG Recognition is NP-complete. A second goal is to provide a theoretical framework within which to better understand the wide range of existing GPSG models, embodied in formal definitions as well as in implemented computer programs. A grammar for English and an informal explanation of the GPSG/RGPSG syntactic features are included in appendices.

A Circuit Grammar For Operational Amplifier Design

Electrical circuit designers seldom create really new topologies or use old ones in a novel way. Most designs are known combinations of common configurations tailored for the particular problem at hand. In this thesis I show that much of the behavior of a designer engaged in such ordinary design can be modelled by a clearly defined computational mechanism executing a set of stylized rules. Each of my rules embodies a particular piece of the designer's knowledge. A circuit is represented as a hierarchy of abstract objects, each of which is composed of other objects. The leaves of this tree represent the physical devices from which physical circuits are fabricated. By analogy with context-free languages, a class of circuits is generated by a phrase-structure grammar of which each rule describes how one type of abstract object can be expanded into a combination of more concrete parts. Circuits are designed by first postulating an abstract object which meets the particular design requirements. This object is then expanded into a concrete circuit by successive refinement using rules of my grammar. There are in general many rules which can be used to expand a given abstract component. Analysis must be done at each level of the expansion to constrain the search to a reasonable set. Thus the rule of my circuit grammar provide constraints which allow the approximate qualitative analysis of partially instantiated circuits. Later, more careful analysis in terms of more concrete components may lead to the rejection of a line of expansion which at first looked promising. I provide special failure rules to direct the repair in this case.

Teaching English tense and aspect with the help of cognitive grammar: An empirical study

Form-focused instruction is usually based on traditional practical/pedagogical grammar descriptions of grammatical features. The comparison of such traditional accounts with cognitive grammar (CG) descriptions seems to favor CG as a basis of pedagogical rules. This is due to the insistence of CG on the meaningfulness of grammar and its detailed analyses of the meanings of particular grammatical features. The differences between traditional and CG rules/descriptions are exemplified by juxtaposing the two kinds of principles concerning the use of the present simple and present progressive to refer to situations happening or existing at speech time. The descriptions provided the bases for the instructional treatment in a quasi-experimental study exploring the effectiveness of using CG descriptions of the two tenses, and of their interplay with stative (imperfective) and dynamic (perfective) verbs, and comparing this effectiveness with the value of grammar teaching relying on traditional accounts found in standard pedagogical grammars. The study involved 50 participants divided into three groups, with one of them constituting the control group and the other two being experimental ones. One of the latter received treatment based on CG descriptions and the other on traditional accounts. CG-based instruction was found to be at least moderately effective in terms of fostering mostly explicit grammatical knowledge and its effectiveness turned out be comparable to that of teaching based on traditional descriptions.

The Advanced Students’ Motivation for Explicit Learning of Grammar

The role of the explicit teaching and learning of grammar in the process of foreign language teaching is a very controversial issue. Language teachers and language teaching methodologists differ in their opinions as to whether grammar should be taught at all and, if so, what is the best way of presenting it. In order to find out students’ opinions on the usefulness of the explicit teaching and learning of grammar a questionnaire was given to fifty-one English majors studying in the Institute of English at Adam Mickiewicz University in Poznań. The results of the questionnaire reveal a very positive attitude on the part of the students towards the explicit learning of grammar. The deductive way of learning seems to be favoured by the majority of the respondents, who also unanimously point to translation as a very effective form of grammar exercise.

Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.

A systematic approach to institutional analysis: Applying Crawford and Ostrom's grammar

In 1995, Crawford and Ostrom proposed a grammatical syntax for examining institutional statements (i.e., rules, norms, and strategies) as part of the institutional analysis and development framework. This article constitutes the first attempt at applying the grammatical syntax to code institutional statements using two pieces of U.S. legislation. The authors illustrate how the grammatical syntax can serve as a basis for collecting, presenting, and analyzing data in a way that is reliable and conveys valid and substantive meaning for the researcher. The article concludes by describing some implementation challenges and ideas for future theoretical and field research. © 2010 University of Utah.

Dissecting Policy Designs: An Application of the Institutional Grammar Tool

An enduring challenge for the policy and political sciences is valid and reliable depiction of policy designs. One emerging approach for dissecting policy designs is the application of Sue Crawford and Elinor Ostrom's institutional grammar tool. The grammar tool offers a method to identify, systematically, the core elements that comprise policies, including target audiences, expected patterns of behavior, and formal modes of sanctioning for noncompliance. This article provides three contributions to the study of policy designs by developing and applying the institutional grammar tool. First, we provide revised guidelines for applying the institutional grammar tool to the study of policy design. Second, an additional component to the grammar, called the oBject, is introduced. Third, we apply the modified grammar tool to four policies that shape Colorado State Aquaculture to demonstrate its effectiveness and utility in illuminating institutional linkages across levels of analysis. The conclusion summarizes the contributions of the article as well as points to future research and applications of the institutional grammar tool. © 2011 Policy Studies Organization.

Using the institutional grammar tool to understand regulatory compliance: The case of Colorado aquaculture

What is the relationship between the design of regulations and levels of individual compliance? To answer this question, Crawford and Ostrom's institutional grammar tool is used to deconstruct regulations governing the aquaculture industry in Colorado, USA. Compliance with the deconstructed regulatory components is then assessed based on the perceptions of the appropriateness of the regulations, involvement in designing the regulations, and intrinsic and extrinsic motivations. The findings suggest that levels of compliance with regulations vary across and within individuals regarding various aspects of the regulatory components. As expected, the level of compliance is affected by the perceived appropriateness of regulations, participation in designing the regulations, and feelings of guilt and fear of social disapproval. Furthermore, there is a strong degree of interdependence among the written components, as identified by the institutional grammar tool, in affecting compliance levels. The paper contributes to the regulation and compliance literature by illustrating the utility of the institutional grammar tool in understanding regulatory content, applying a new Q-Sort technique for measuring individual levels of compliance, and providing a rare exploration into feelings of guilt and fear outside of the laboratory setting. © 2012 Blackwell Publishing Asia Pty Ltd.

Division of labor in frontal eye field neurons during presaccadic remapping of visual receptive fields.

Our percept of visual stability across saccadic eye movements may be mediated by presaccadic remapping. Just before a saccade, neurons that remap become visually responsive at a future field (FF), which anticipates the saccade vector. Hence, the neurons use corollary discharge of saccades. Many of the neurons also decrease their response at the receptive field (RF). Presaccadic remapping occurs in several brain areas including the frontal eye field (FEF), which receives corollary discharge of saccades in its layer IV from a collicular-thalamic pathway. We studied, at two levels, the microcircuitry of remapping in the FEF. At the laminar level, we compared remapping between layers IV and V. At the cellular level, we compared remapping between different neuron types of layer IV. In the FEF in four monkeys (Macaca mulatta), we identified 27 layer IV neurons with orthodromic stimulation and 57 layer V neurons with antidromic stimulation from the superior colliculus. With the use of established criteria, we classified the layer IV neurons as putative excitatory (n = 11), putative inhibitory (n = 12), or ambiguous (n = 4). We found that just before a saccade, putative excitatory neurons increased their visual response at the RF, putative inhibitory neurons showed no change, and ambiguous neurons increased their visual response at the FF. None of the neurons showed presaccadic visual changes at both RF and FF. In contrast, neurons in layer V showed full remapping (at both the RF and FF). Our data suggest that elemental signals for remapping are distributed across neuron types in early cortical processing and combined in later stages of cortical microcircuitry.

Dynamics of visual receptive fields in the macaque frontal eye field.

Neuronal receptive fields (RFs) provide the foundation for understanding systems-level sensory processing. In early visual areas, investigators have mapped RFs in detail using stochastic stimuli and sophisticated analytical approaches. Much less is known about RFs in prefrontal cortex. Visual stimuli used for mapping RFs in prefrontal cortex tend to cover a small range of spatial and temporal parameters, making it difficult to understand their role in visual processing. To address these shortcomings, we implemented a generalized linear model to measure the RFs of neurons in the macaque frontal eye field (FEF) in response to sparse, full-field stimuli. Our high-resolution, probabilistic approach tracked the evolution of RFs during passive fixation, and we validated our results against conventional measures. We found that FEF neurons exhibited a surprising level of sensitivity to stimuli presented as briefly as 10 ms or to multiple dots presented simultaneously, suggesting that FEF visual responses are more precise than previously appreciated. FEF RF spatial structures were largely maintained over time and between stimulus conditions. Our results demonstrate that the application of probabilistic RF mapping to FEF and similar association areas is an important tool for clarifying the neuronal mechanisms of cognition.