When research articles introduce new results or findings they typically relate them only to knowledge entities of immediate relevance. However, a large body of context knowledge related to the results is often not exp...
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ISBN:
(纸本)9781605581644
When research articles introduce new results or findings they typically relate them only to knowledge entities of immediate relevance. However, a large body of context knowledge related to the results is often not explicitly mentioned in the article. To overcome this limitation the state-of-the-art information retrieval approaches rely on the latent semantic analysis in which terms in articles are projected to a lower dimensional latent space and best possible matches in this space are identified. However, this approach may not perform well enough if the number of explicit knowledge entities in the articles is too small compared to the amount of knowledge in the domain. We address the problem by exploiting a domain knowledge layer, a rich network of relations among knowledge entities in the domain extracted from a large corpus of documents. The knowledge layer supplies the context knowledge that lets us relate different knowledge entities and hence improve the information retrieval performance. We develop and study a new framework for i) learning and aggregating the relations in the knowledge layer from the literature corpus;ii) and for exploiting these relations to improve the information-retrieval of relevant documents. We demonstrate the benefit of the method on biomedical text retrievals.
The paper gives a statement and considers the solution of an urgent scientific problem of formation control for a group of unmanned aerial vehicles (UAVs) operating in an unstable environment. To construct the referen...
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In this paper we present a detailed scheme for annotating expressions of opinions, beliefs, emotions, sentiment and speculation (private states) in the news and other discourse. We explore inter-annotator agreement fo...
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in th...
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Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly in-feasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the problem has been made in the literature. One of the state-of-the-art methods that has been recently introduced is the Variational Dual-Tree (VDT) framework. Despite some of its unique features, VDT is currently restricted only to Euclidean spaces where the Euclidean distance quantifies the similarity. In this paper, we extend the VDT framework beyond the Euclidean distance to more general Bregman divergences that include the Euclidean distance as a special case. By exploiting the properties of the general Bregman divergence, we show how the new framework can maintain all the pivotal features of the VDT framework and yet significantly improve its performance in non-Euclidean domains. We apply the proposed framework to different text categorization problems and demonstrate its benefits over the original VDT.
A key question in conditional planning is: how many, and which of the possible execution failures should be planned for? One cannot, in general, plan for all the failures that can be anticipated: there are simply too ...
ISBN:
(纸本)3540639128
A key question in conditional planning is: how many, and which of the possible execution failures should be planned for? One cannot, in general, plan for all the failures that can be anticipated: there are simply too many. But neither can one ignore all the possible failures, or one will fail to produce sufficiently flexible plans. We describe a planning system that attempts to identify the contingencies that contribute the most to a plan's overall value. Plan generation proceeds by extending the plan to include actions that will be taken in case the identified contingencies fail, iterating until either a given expected value threshold is reached or the planning time is exhausted. We provide details of the algorithm, discuss its implementation in the Mahinur system, and give initial results of experiments comparing it with the C-Buridan approach to conditional planning.
This work proposes opinion frames as a representation of discourse-level associations which arise from related opinion topics. We illustrate how opinion frames help gather more information and also assist disambiguati...
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This paper studies multi-label classification problem in which data instances are associated with multiple, possibly high-dimensional, label vectors. This problem is especially challenging when labels are dependent an...
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Generic reflexive statements such as Elephants love themselves have traditionally been formalized using some variant of predicate logic, with variables to mark coreferentiality. We present a radically different semant...
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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...
This work proposes opinion frames as a representation of discourse-level associations that arise from related opinion targets and which are common in task-oriented meeting dialogs. We define the opinion frames and exp...
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