12859800
Building an information retrieval test collection for spontaneous conversational speech
Test collections model use cases in ways that facilitate evaluation of information retrieval systems. This paper describes the use of search-guided relevance assessment to create a test collection for retrieval of spontaneous conversational speech. Approximately 10,000 thematically coherent segments were manually identified in 625 hours of oral history interviews with 246 individuals. Automatic speech recognition results, manually prepared summaries, controlled vocabulary indexing, and name authority control are available for every segment. Those features were leveraged by a team of four relevance assessors to identify topically relevant segments for 28 topics developed from actual user requests. Search-guided assessment yielded sufficient inter-annotator agreement to support formative evaluation during system development. Baseline results for ranked retrieval are presented to illustrate use of the collection.
414995
Combining bidirectional translation and synonymy for cross-language information retrieval
This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.
34826531
User-assisted query translation for interactive CLIR
We view interactive Cross-Language Information Retrieval (CLIR) as an iterative process in which the searcher and the retrieval system collaborate to find documents that satisfy the searcher’s needs, regardless of the language in which those documents are written. Our motivation is that humans and machines can bring complementary strengths to this process. Machines are excellent at repetitive tasks that are well specified; humans bring creativity and exceptional pattern recognition capabilities. Properly coupling these capabilities can result in a synergy that greatly exceeds the ability of either human or machine alone. Designers of CLIR systems can select from a variety of fully automatic techniques to overcome problems with unknown terms and translation ambiguity, but automatic processing of this sort risks reducing the searcher’s understanding of system operation. This, in turn, tends to work against the synergy that we seek to accomplish. We are therefore exploring more transparent approaches to support interactive CLIR.
411372
Stratification Approach to Modeling Video
The explosive growth of audiovisual information in the last few years has made the development of advanced video modeling and management tools an urgent task. In this research, we investigate the use of stratification approach to model the contextual information of video contents as multi-layered strata. By judiciously choosing the right level and types of strata, we are able to automate a major part of the strata extraction process. Using the strata as the basis, we have developed advanced functionalities to support the flexible retrieval and content-based browsing of video. A prototype has been developed to support the whole process of video management, from strata extraction, to indexing, retrieval and browsing. The prototype is tested in the domain of news video and the system has been found to be effective.
9293980
Topology modeling via cluster graphs
Several recent studies have focused on generating Internet topology graphs. Topology graphs have been used to predict growth patterns of prefixes and traffic flow as well as for designing better protocols. Internet topology graphs can be studied at eitherm inter-domain level or router level. For some applications, inter-domain level topology graph is too coarse, while router level topology graph may be too fine-grained. We introduce cluster graphs as a way of modeling Internet topology at an intermediate level of granularity and compare it against inter-domain and router graphs.
35286842
On the NP-Isomorphism Problem with Respect to Random Instances
This paper takes a new approach to studying the NP-isomorphism problem initiated by Berman and Hartmanis. We consider polynomial-time computable and invertible bijections among NP-complete sets that preserve the underlying natural distributions on random instances. We show that there are natural NP-complete problems which are not polynomially isomorphic when instances are chosen at random because no polynomial-time isomorphisms can preserve the underlying natural distributions on instances of these problems, On the other hand, we show that all the known average-case NP-complete problems under many-one reductions are polynomially isomorphic with respect to their distributions on random instances. Simple and accessible proofs to the known average-case NP-complete problems are presented when establishing the isomorphism result. New average-case NP-complete problems are also presented.
18656960
A dual neural network for kinematic control of redundant robot manipulators
The inverse kinematics problem in robotics can be formulated as a time-varying quadratic optimization problem. A new recurrent neural network, called the dual network, is presented in this paper. The proposed neural network is composed of a single layer of neurons, and the number of neurons is equal to the dimensionality of the workspace. The proposed dual network is proven to be globally exponentially stable. The proposed dual network is also shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.
10338450
CLUE: cluster-based retrieval of images by unsupervised learning
In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.