5 research outputs found
Critiquing: Effective Decision Support in Time-Critical Domains (Dissertation Proposal)
The effective communication of information is an important concern in the design of an expert consultation system. Several researchers have chosen to adopt a critiquing mode, in which the system evaluates and reacts to a solution proposed by the user rather than presenting its own solution. In this proposal, I present an architecture for a critiquing system that functions in real-time, during the process of developing and executing a management plan in time-critical situations. The architecture is able to take account of and reason about multiple, interacting goals and to identify critical errors in the proposed management plan. This architecture is being implemented as part of the TraumAID system for the management of patients with severe injuries
Research in the Language, Information and Computation Laboratory of the University of Pennsylvania
This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania.
It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition.
Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue itâs easier than ever to do so: this document is accessible on the âinformation superhighwayâ. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html
In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authorsâ abstracts in the web version of this report.
The abstracts describe the researchersâ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn
Developing an enriched natural language grammar for prosodically-improved concent-to-speech synthesis
The need for interacting with machines using spoken natural language is growing,
along with the expectation that synthetic speech in this context sound
natural. Such interaction includes answering questions, where prosody plays an
important role in producing natural English synthetic speech by communicating
the information structure of utterances.
CCG is a theoretical framework that exploits the notion that, in English, information
structure, prosodic structure and syntactic structure are isomorphic.
This provides a way to convert a semantic representation of an utterance into
a prosodically natural spoken utterance. GF is a framework for writing grammars,
where abstract tree structures capture the semantic structure and concrete
grammars render these structures in linearised strings. This research combines
these frameworks to develop a system that converts semantic representations
of utterances into linearised strings of natural language that are marked up to
inform the prosody-generating component of a speech synthesis system.ComputingM. Sc. (Computing
Using Context To Specify Intonation In Speech Synthesis
A generator based on Combinatory Categorial Grammar using a simple and domain-independent discourse model can be used to direct synthesis of intonation contours for responses to data-base queries, conveying distinctions of contrast and emphasis determined by the discourse model and the state of the knowledge-base