61042466
The berkeley UNIX consultant project
UC (UNIX Consultant) is an intelligent, natural language interface that allows naive users to learn about the UNIX2 operating system. UC was undertaken because the task was thought to be both a fertile domain for artificial intelligence (AI) research and a useful application of AI work in planning, reasoning, natural language processing, and knowledge representation.The current implementation of UC comprises the following components: a language analyzer, called ALANA, produces a representation of the content contained in an utterance; an inference component, called a concretion mechanism, that further refines this content; a goal analyzer, PAGAN, that hypothesizes the plans and goals under which the user is operating; an agent, called UCEgo, that decides on UC's goals and proposes plans for them; a domain planner, called KIP, that computes a plan to address the user's request; an expression mechanism, UCExpress, that determines the content to be communicated to the user, and a language production mechanism, UCGen, that expresses UC's response in English.UC also contains a component, called KNOME, that builds a model of the user's knowledge state with respect to UNIX. Another mechanism, UCTeacher, allows a user to add knowledge of both English vocabulary and facts about UNIX to UC's knowledge base. This is done by interacting with the user in natural language.All these aspects of UC make use of knowledge represented in a knowledge representation system called KODIAK. KODIAK is a relation-oriented system that is intended to have wide representational range and a clear semantics, while maintaining a cognitive appeal. All of UC's knowledge, ranging from its most general concepts to the content of a particular utterance, is represented in KODIAK.
8592573
Contextual Spelling Correction Using Latent Semantic Analysis
Contextual spelling errors are defined as the use of an incorrect, though valid, word in a particular sentence or context. Traditional spelling checkers flag misspelled words, but they do not typically attempt to identify words that are used incorrectly in a sentence. We explore the use of Latent Semantic Analysis for correcting these incorrectly used words and the results are compared to earlier work based on a Bayesian classifier.
15685117
MetaBank: A Knowledge-Base of Metaphoric Language Conventions
The frequent and conventional use of nonliteral language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy, and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about nonliteral language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge-base of English metaphorical conventions. This article describes our three-part approach to the construction of MetaBank: the collection of on-line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge-base based on the preceding analyses.
39279518
Representing UNIX Domain Metaphors
The language used to describe technical domains like UNIX is filled with metaphor. An approach to metaphor, based on theexplicit representation of knowledge about metaphors, has beendeveloped. MIDAS (Metaphor Interpretation, Denotation, andAcquisition System) is a computer program that that has been developedbased upon this approach. MIDAS can be used to representknowledge about conventional metaphors, interpret metaphoric languageby applying this knowledge, and dynamically learn new metaphors asthey are encountered during normal processing.
2946678
Conventional Metaphor and the Lexicon
Metaphor and other forms of non-literal language are essential parts of language which have direct bearing on theories of lexical semantics. Neither narrow theories of lexical semantics, nor theories relying solely on world knowledge are sufficient to account for our ability to generate and interpret non-literal language. This paper presents an emerging approach that may provide such an account. This approach is based on systematic representations that capture non-literal language conventions, and mechanisms that can dynamically understand and learn new uses as they are encountered.
26598527
Large Tandem Queueing Networks with Blocking
Systems consisting of many queues in series have been considered by Glynn and Whitt (1991) and Baccelli, Borovkov and Mairesse (2000). We extend their results to apply to situations where the queues have finite capacity and so various types of “blocking” can occur. The models correspond to max-plus type recursions, of simple form but in infinitely many dimensions; they are related to “percolation” problems of finding paths of maximum weight through a 2-dimensional lattice with random weights at the vertices. Topics treated include: laws of large numbers for the speed of customers progressing through the system; stationary behaviour for systems with external arrival processes; a functional central limit theorem describing the behaviour of the “front of the wave” progressing through a system which starts empty; stochastic orderings for waiting times of customers at successive queues. Several open problems are noted.
14471368
A Unified Framework for Constructing Multimodal Experiments and Applications
Inspired by a Wizard of Oz (WOZ) simulation experiment, we developed a working prototype of a system that enables users to interact with a map display through synergistic combinations of pen and voice. To address many of the issues raised by multimodal fusion, our implementation employed a distributed multi-agent framework to coordinate parallel competition and cooperation among processing components. Since then, the agent-based infrastructure has been enhanced with a collaboration technology, creating a framework in which multiple humans and automated agents can naturally interact within the same graphical workspace.Our current endeavour is the leveraging of this architecture to create a unified implementation framework for simultaneously developing both WOZ simulated systems and their fully-automated counterparts. Bootstrapping effects made possible by such an approach are illustrated by an experiment currently under way in our laboratory: as a naive subject draws, writes, and speaks requests to a (simulated) interactive map, a hidden Wizard responds as efficiently as possible using our best fullyautomated system, through either standard graphical interface devices or multimodal combinations of pen and voice. The input choices made by both subject and Wizard are invaluable, and the data collected from each can be applied directly to evaluating and improving the automated part of the system.
6534012
PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine
BackgroundThe majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.ResultsCross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.ConclusionsMachine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.
731954
Separating agreement from execution for byzantine fault tolerant services
We describe a new architecture for Byzantine fault tolerant state machine replication that separates agreement that orders requests from execution that processes requests. This separation yields two fundamental and practically significant advantages over previous architectures. First, it reduces replication costs because the new architecture can tolerate faults in up to half of the state machine replicas that execute requests. Previous systems can tolerate faults in at most a third of the combined agreement/state machine replicas. Second, separating agreement from execution allows a general privacy firewall architecture to protect confidentiality through replication. In contrast, replication in previous systems hurts confidentiality because exploiting the weakest replica can be sufficient to compromise the system. We have constructed a prototype and evaluated it running both microbenchmarks and an NFS server. Overall, we find that the architecture adds modest latencies to unreplicated systems and that its performance is competitive with existing Byzantine fault tolerant systems.
8716869
Generating product-lines of product-families
GenVoca is a methodology and technology for generating product-lines, i.e. building variants of a program. The primitive components from which applications are constructed are refinements or layers, which are modules that implement a feature that many programs of a product-line can share. Unlike conventional components (e.g., COM, CORBA, EJB), a layer encapsulates fragments of multiple classes. Sets of fully formed classes can be produced by composing layers. Layers are modular, albeit unconventional, building blocks of programs. But what are the building blocks of layers? We argue that facets is an answer. A facet encapsulates fragments of multiple layers, and compositions of facets yields sets of fully formed layers. Facets arise when refinements scale from producing variants of individual programs to producing variants of multiple integrated programs, as typified byproduct families (e.g., MS Office). We present a mathematical model that explains relationships between layers and facets. We use the model to develop a generator for tools (i.e., product family) that are used in language-extensible Integrated Development Environments (IDEs).