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.
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.
14531338
Building a Foundation System for Producing Short Answers to Factual Questions
In this paper we describe question answering research being pursued as a joint project between Columbia University and the University of Colorado at Boulder as part of ARDA’s AQUAINT program. As a foundation for targeting complex questions involving opinions, events, and paragraph-length answers, we recently built two systems for answering short factual questions. We submitted results from the two systems to TREC’s Q&A track, and the bulk of this paper describes the methods used in building each system and the results obtained. We conclude by discussing current work aiming at combining modules from the two systems in a unified, more accurate system and adding capabilities for producing complex answers in addition to short ones.
15290012
Shallow Semantic Parsing using Support Vector Machines
In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.
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.
618620
Corpus-based static branch prediction
Correctly predicting the direction that branches will take is increasingly important in today's wide-issue computer architectures. The name program-based branch prediction is given to static branch prediction techniques that base their prediction on a program's structure. In this paper, we investigate a new approach to program-based branch prediction that uses a body of existing programs to predict the branch behavior in a new program. We call this approach to program-based branch prediction, evidence-based static prediction, or ESP. The main idea of ESP is that the behavior of a corpus of programs can be used to infer the behavior of new programs. In this paper, we use a neural network to map static features associated with each branch to the probability that the branch will be taken. ESP shows significant advantages over other prediction mechanisms. Specifically, it is a program-based technique, it is effective across a range of programming languages and programming styles, and it does not rely on the use of expert-defined heuristics. 
In this paper, we describe the application of ESP to the problem of branch prediction and compare our results to existing program-based branch predictors. We also investigate the applicability of ESP across computer architectures, programming languages, compilers, and run-time systems. Averaging over a body of 43 C and Fortran programs, ESP branch prediction results in a miss rate of 20%, as compared with the 25% miss rate obtained using the best existing program-based heuristics.
7785482
Strategies for Migration from C to Java
With the growing popularity of the Java programming language for both client and server side applications in network centric computing, there is a rising need for programming libraries that can be easily integrated into Java programs. Many mature programming libraries exist for the C programming language. This paper surveys approaches to the migration of C source code to Java and evaluates their usefulness with respect to the integration of the migrated code into Java programs.
14705463
C to Java migration experiences
With the growing popularity of the Java programming language for both client and server side applications in network-centric computing, there is a rising need for programming libraries that can be easily integrated into Java programs. In a previous paper, we surveyed current strategies for integrating C source code into Java programs, pointed out their weaknesses and presented goals for an improved migration approach. In this paper, we present the Ephedra approach to software migration and report on the results of three case studies transliterating C source code to Java using the Ephedra environment.
206630030
Statistical Gesture Recognition Through Modelling of Parameter Trajectories
The recognition of human gestures is a challenging problem that can contribute to a natural man-machine interface. In this paper, we present a new technique for gesture recognition. Gestures are modelled as temporal trajectories of parameters. Local sub-sequences of these trajectories are extracted and used to define an orthogonal space using principal component analysis. In this space the probabilistic density function of the training trajectories is represented by a multidimensional histogram, which builds the basis for the recognition. Experiments on three different recognition problems show the general utility of the approach.
12923227
Levels of Representation in the Annotation of Emotion for the Specification of Expressivity in ECAs
In this paper we present a two-steps approach towards the creation of affective Embodied Conversational Agents (ECAs): annotation of a real-life nonacted emotional corpus and animation by copy-synthesis. The basis of our approach is to study how coders perceive and annotate at several levels the emotions observed in a corpus of emotionally rich TV video interviews. We use their annotations to specify the expressive behavior of an agent at several levels. We explain how such an approach can be useful for providing knowledge as input for the specification of non-basic patterns of emotional behaviors to be displayed by the ECA (e.g. which perceptual cues and levels of annotation are required for enabling the proper recognition of the emotions).
5177142
Spectral techniques for graph bisection in genetic algorithms
Various applications of spectral techniques for enhancing graph bisection in genetic algorithms are investigated. Several enhancements to a genetic algorithm for graph bisection are introduced based on spectral decompositions of adjacency matrices of graphs and subpopulation matrices. First, the spectral decompositions give initial populations for the genetic algorithm to start with. Next, spectral techniques are used to engineer new individuals and reorder the schema to strategically group certain sets of vertices together on the chromosome. The operators and techniques are found to be beneficial when added to a plain genetic algorithm and when used in conjunction with other local optimization techniques for graph bisection. In addition, several world record minimum bisections have been obtained from the methods described in this study.