Query Routing is a critical step in P2P Information Retrieval. In this paper, we consider learning to rank approaches for query routing in the clustered P2P IR architecture. Our formulation, LTRo, scores resources based on the number of relevant documents for each training query, and uses that information to build a model that would then rank promising peers for a new query. Our empirical analy...

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This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistical...

We investigate the problem of ordering medical events in unstructured clinical narratives by learning to rank them based on their time of occurrence. We represent each medical event as a time duration, with a corresponding start and stop, and learn to rank the starts/stops based on their proximity to the admission date. Such a representation allows us to learn all of Allen’s temporal relations ...

We study the droplet that results from conditioning the planar subcritical Fortuin-Kasteleyn random cluster model on the presence of an open circuit Γ0 encircling the origin and enclosing an area of at least (or exactly) n . We consider local deviation of the droplet boundary, measured in a radial sense by the maximum local roughness, MLR ( Γ0 ) , this being the maximum distance from a point in...

This paper describes our approaches to the TREC 2012 Microblog Track. We explore the query expansion and document expansion techniques to address the retrieval of short tweet texts. Further, we examine the webpages linked by the URL in a tweet as an external source to improve the performance. Then learning to rank technique is adopted to combine all features for better performance. Finally, we ...

Œe e‚ective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing aŠention for its ability to automatically learning features from raw text; thus, many deep IR models have been proposed recently. However, the learning process of these deep IR models resemble a black box. Œerefore, it is necessary to identify the di‚erence between autom...

I let q ∈ {0, 1} and d ∈ {0, 1} be query and document vectors, dimensions indicating word occurrence for dictionaries of size Q and D I score function f(q,d) = qWd = ∑Q i=1 ∑D j=1 qiWijdj , where W ∈ RQ×D is a matrix of word associations between query and document dictionaries I R is a set of tuples (q,d+,d−), document d being more relevant for query q than d− I relevance rank rq,d, rank di ere...

Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed at different levels of granularity is pro...