303 research outputs found
A Processing Model for Free Word Order Languages
Like many verb-final languages, Germn displays considerable word-order
freedom: there is no syntactic constraint on the ordering of the nominal
arguments of a verb, as long as the verb remains in final position. This effect
is referred to as ``scrambling'', and is interpreted in transformational
frameworks as leftward movement of the arguments. Furthermore, arguments from
an embedded clause may move out of their clause; this effect is referred to as
``long-distance scrambling''. While scrambling has recently received
considerable attention in the syntactic literature, the status of long-distance
scrambling has only rarely been addressed. The reason for this is the
problematic status of the data: not only is long-distance scrambling highly
dependent on pragmatic context, it also is strongly subject to degradation due
to processing constraints. As in the case of center-embedding, it is not
immediately clear whether to assume that observed unacceptability of highly
complex sentences is due to grammatical restrictions, or whether we should
assume that the competence grammar does not place any restrictions on
scrambling (and that, therefore, all such sentences are in fact grammatical),
and the unacceptability of some (or most) of the grammatically possible word
orders is due to processing limitations. In this paper, we will argue for the
second view by presenting a processing model for German.Comment: 23 pages, uuencoded compressed ps file. In {\em Perspectives on
Sentence Processing}, C. Clifton, Jr., L. Frazier and K. Rayner, editors.
Lawrence Erlbaum Associates, 199
Encoding Lexicalized Tree Adjoining Grammars with a Nonmonotonic Inheritance Hierarchy
This paper shows how DATR, a widely used formal language for lexical
knowledge representation, can be used to define an LTAG lexicon as an
inheritance hierarchy with internal lexical rules. A bottom-up featural
encoding is used for LTAG trees and this allows lexical rules to be implemented
as covariation constraints within feature structures. Such an approach
eliminates the considerable redundancy otherwise associated with an LTAG
lexicon.Comment: Latex source, needs aclap.sty, 8 page
Concurrent Lexicalized Dependency Parsing: The ParseTalk Model
A grammar model for concurrent, object-oriented natural language parsing is
introduced. Complete lexical distribution of grammatical knowledge is achieved
building upon the head-oriented notions of valency and dependency, while
inheritance mechanisms are used to capture lexical generalizations. The
underlying concurrent computation model relies upon the actor paradigm. We
consider message passing protocols for establishing dependency relations and
ambiguity handling.Comment: 90kB, 7pages Postscrip
An Alternative Conception of Tree-Adjoining Derivation
The precise formulation of derivation for tree-adjoining grammars has
important ramifications for a wide variety of uses of the formalism, from
syntactic analysis to semantic interpretation and statistical language
modeling. We argue that the definition of tree-adjoining derivation must be
reformulated in order to manifest the proper linguistic dependencies in
derivations. The particular proposal is both precisely characterizable through
a definition of TAG derivations as equivalence classes of ordered derivation
trees, and computationally operational, by virtue of a compilation to linear
indexed grammars together with an efficient algorithm for recognition and
parsing according to the compiled grammar.Comment: 33 page
Structure Unification Grammar: A Unifying Framework for Investigating Natural Language
This thesis presents Structure Unification Grammar and demonstrates its suitability as a framework for investigating natural language from a variety of perspectives. Structure Unification Grammar is a linguistic formalism which represents grammatical information as partial descriptions of phrase structure trees, and combines these descriptions by equating their phrase structure tree nodes. This process can be depicted by taking a set of transparencies which each contain a picture of a tree fragment, and overlaying them so they form a picture of a complete phrase structure tree. The nodes which overlap in the resulting picture are those which are equated. The flexibility with which information can be specified in the descriptions of trees and the generality of the combination operation allows a grammar writer or parser to specify exactly what is known where it is known. The specification of grammatical constraints is not restricted to any particular structural or informational domains. This property provides for a very perspicuous representation of grammatical information, and for the representations necessary for incremental parsing.
The perspicuity of SUG\u27s representation is complemented by its high formal power. The formal power of SUG allows other linguistic formalisms to be expressed in it. By themselves these translations are not terribly interesting, but the perspicuity of SUG\u27s representation often allows the central insights of the other investigations to be expressed perspicuously in SUG. Through this process it is possible to unify the insights from a diverse collection of investigations within a single framework, thus furthering our understanding of natural language as a whole. This thesis gives several examples of how insights from investigations into natural language can be captured in SUG. Since these investigations come from a variety of perspectives on natural language, these examples demonstrate that SUG can be used as a unifying framework for investigating natural language
An alternative conception of tree-adjoining derivation
The precise formulation of derivation for tree-adjoining grammars has important ramifications for a wide variety of uses of the formalism, from syntactic analysis to semantic interpretation and statistical language modeling. We argue that the definition of tree-adjoining derivation must be reformulated in order to manifest the proper linguistic dependencies in derivations. The particular proposal is both precisely characterizable, through a compilation to linear indexed grammars, and computationally operational, by virtue of an efficient algorithm for recognition and parsing.Engineering and Applied Science
CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research.
The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language.
Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions.
There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization.
The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator.
As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.)
Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace.
We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent
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