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.
Ungrammatical sentences present challenges for statistical parsers because the well-formed trees they produce may not be appropriate for these sentences. We introduce a framework for reviewing the parses of ungrammati...
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Ungrammatical sentences present challenges for statistical parsers because the well-formed trees they produce may not be appropriate for these sentences. We introduce a framework for reviewing the parses of ungrammatical sentences and extracting the coherent parts whose syntactic analyses make sense. We call this task parse tree fragmentation. In this paper, we propose a training methodology for fragmenting parse trees without using a task-specific annotated corpus. We also propose some fragmentation strategies and compare their performance on an extrinsic task - fluency judgments in two domains: English-as-a-Second Language (ESL) and machine translation (MT). Experimental results show that the proposed fragmentation strategies are competitive with existing methods for making fluency judgments;they also suggest that the overall framework is a promising way to handle syntactically unusual sentences.
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALF). The main idea of the approach is to approximate the ...
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This paper presents an annotation scheme for adding entity and event target annotations to the MPQA corpus, a rich span-annotated opinion corpus. The new corpus promises to be a valuable new resource for developing sy...
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Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALF). The main idea of the approach is to approximate the ...
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In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especiall...
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In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especially those dealing with the analysis of the wild animal cases discussed in Berman and Hafner's 1993 ICAIL article. We review the basic ideas about dimensions, as used in HYPO, and point out differences with factors, as used in subsequent systems like CATO. Our goal is to correct certain misconceptions that have arisen over the years.
The goal of this work is recognizing opinionated and evaluative (subjective) language in text. The ability to recognize such language would be beneficial for many NLP applications such as question answering, informati...
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This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are mor...
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When research articles introduce new findings or concepts they typically relate them only to knowledge and domain concepts of immediate relevance. However, many domain concepts relevant for the article and its finding...
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