This paper studies the impact that difficult-to-translate source-language phrases might have on the machine translation process. We formulate the notion of difficulty as a measurable quantity;we show that a classifier...
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This paper analyzes opinion categories like Sentiment and Arguing in meetings. We first annotate the categories manually. We then develop genre-specific lexicons using interesting function word combinations for detect...
In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm (EPIS-BN) (Yuan & Druzdzel 2003;2006b) and show that its importance function minimizes t...
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Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distrib...
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Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.
Knowledge elicitation is difficult for expert systems that are based on probability theory. The elicitation of probabilities for a probabilistic model requires a lot of time and interaction between the knowledge engin...
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Finding relevant information in a hyperspace has been a much studied problem for many years. With the emergence of so called Web 2.0 technologies we have seen the use of social systems for retrieval tasks increasing d...
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ISBN:
(纸本)1595938206
Finding relevant information in a hyperspace has been a much studied problem for many years. With the emergence of so called Web 2.0 technologies we have seen the use of social systems for retrieval tasks increasing dramatically. Each system collects and exploits its own pool of community wisdom for the benefit of its users. In this paper we suggest a form of retrieval which exploits the pools of wisdom of multiple social technologies, specifically social search and social navigation. The paper details the added user benefits of merging several sources of social wisdom. We present details of the ASSIST engine developed to integrate social support mechanisms for the users of information repositories. The goal of this paper is to present the main features of the integrated community-based personalization engine that we have developed in order to improve retrieval in the hyperspace of information resources. It also reports the results of an empirical study of this technology. Copyright 2007 ACM.
This paper explores what kind of user simulation model is suitable for developing a training corpus for using Markov Decision Processes (MDPs) to automatically learn dialog strategies. Our results suggest that with sp...
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This paper describes an intelligent tutoring system, LARGO, that helps students learn skills of legal reasoning with hypotheticals by analyzing oral arguments before the US Supreme Court. The skills involve proposing ...
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ISBN:
(纸本)1595936807
This paper describes an intelligent tutoring system, LARGO, that helps students learn skills of legal reasoning with hypotheticals by analyzing oral arguments before the US Supreme Court. The skills involve proposing a rule-like test for deciding a case, posing hypotheticals to challenge the rule, and responding by analogizing or distinguishing the hypotheticals and/or modifying the proposed test. Students diagram arguments in a special-purpose graphical language and receive feedback in the form of reflection questions. Copyright 2007 ACM.
Empirical spoken dialog research often involves the collection and analysis of a dialog corpus. However, it is not well understood whether and how a corpus of dialogs collected using recruited subjects differs from a ...
Hybrid approximate linear programming (HALP) has recently emerged as a promising approach to solving large factored Markov decision processes (MDPs) with discrete and continuous state and action variables. Its central...
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Hybrid approximate linear programming (HALP) has recently emerged as a promising approach to solving large factored Markov decision processes (MDPs) with discrete and continuous state and action variables. Its central idea is to reformulate initially intractable problem of computing the optimal value function as its linear programming approximation. In this work, we present the HALP framework and discuss several representational and computational issues that make the approach appropriate for large MDPs. We compare three different methods for solving HALP and demonstrate the feasibility of the approach on high-dimensional distributed control problems.
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