, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We

the effectiveness of the various methods. Prescriptions are given for conducting text re-trieval operations iteratively using relevance feedback. Introduction to Relevance Feedback It is well known that the original query formulation process is not transparent to most information system users. In particular

;quot; may be needed for one node to exchange data with another across the network. In recent years, a variety of new routing protocols targeted specifically at this environment have been developed, but little performance information on each protocol and no realistic performance comparison between them

predicated on the belief that information filtering can be more effective when humans are involved in the filtering process. Tapestry was designed to support both content-based filtering and collaborative filtering, which entails people collaborating to help each other perform filtering

and monitoring, and hence for planning and adapting application behavior. We present here an information services architecture that addresses performance, security, scalability, and robustness requirements. Our architecture defines simple low-level enquiry and registration protocols that make it easy

. This suggests that perhaps a better indexing model would help solve the problem. However, we feel that making unwarranted parametric assumptions will not lead to better retrieval performance. Furthermore, making prior assumptions about the similarity of documents is not warranted either. Instead, we propose

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation) , that eciently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high

to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.

philip.resnikfleast.sun.com This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity

We describe a peer-to-peer system which has provable consistency and performance in a fault-prone environment. Our system routes queries and locates nodes using a novel XOR-based metric topology that simplifies the algorithm and facilitates our proof. The topology has the property that every