535520
Detection of Entity Mentions Occuring in English and Chinese Text
In this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language independent system. It is optimal in the sense that all categorical constraints are simultaneously considered. The system is compact and easy to develop and maintain, since only a single set of features and classifiers are needed to be designed and optimized. It is implemented using one-versus-all support vector machine (SVM) classifiers and a number of feature extractors at several linguistic levels. SVMs are well known for their ability to handle a large set of overlapping features with theoretically sound generalization properties. Data sparsity might be an important issue as a result of a large number of classes and relatively moderate training data size. However, we report results that the integrated system performs as good as a pipelined system that decomposes the problem into a few smaller sub-tasks. We conduct all our experiments using ACE 2004 data, evaluate the systems using ACE metrics and report competitive performance.
28422069
Exploration of Coreference Resolution: The ACE Entity Detection and Recognition Task
In this paper, we consider the coreference resolution problem in the context of information extraction as envisioned by the DARPA Automatic Content Extraction (ACE) program Given a set of entity mentions referring to real world entities and a similarity matrix that characterizes how similar those mentions are, we seek a set of entities that are uniquely co-referred to by those entity mentions The quality of the clustering of entity mentions into unique entities significantly depends on the quality of (1) the similarity matrix and (2) the clustering algorithm We explore the coreference resolution problem along those two dimensions and clearly show the tradeoff among several ways of learning similarity matrix and using it while performing clustering.
211611
Automatic Time Expression Labeling for English and Chinese Text
In this paper, we describe systems for automatic labeling of time expressions occurring in English and Chinese text as specified in the ACE Temporal Expression Recognition and Normalization (TERN) task. We cast the chunking of text into time expressions as a tagging problem using a bracketed representation at token level, which takes into account embedded constructs. We adopted a left-to-right, token-by-token, discriminative, deterministic classification scheme to determine the tags for each token. A number of features are created from a predefined context centered at each token and augmented with decisions from a rule-based time expression tagger and/or a statistical time expression tagger trained on different type of text data, assuming they provide complementary information. We trained one-versus-all multi-class classifiers using support vector machines. We participated in the TERN 2004 recognition task and achieved competitive results.
11444892
A prototype implementation of archival Intermemory
An Archival Intermemory solves the problem of highly survivable digital data storage in the spirit of the Internet. In this paper we describe a prototype implementation of Intermemory, including an overall system architecture and implementations of key system components. The result is a working Intermemory that tolerates up to 17 simultaneous node failures, and includes a Web gateway for browser-based access to data. Our work demonstrates the basic feasibility of Intermemory and represents signi cant progress towards a deployable system.
60542101
A novel ant clustering algorithm based on cellular automata
Based on the principle of cellular automata in artificial life, an artificial ant sleeping model (ASM) and an ant algorithm for cluster analysis (A4C) are presented. Inspired by the behaviors of gregarious ant colonies, we use the ant agent to represent a data object. In ASM, each ant has two states: a sleeping state and an active state. The ant's state is controlled by a function of the ant's fitness to the environment it locates and a probability for the ants becoming active. The state of an ant is determined only by its local information. By moving dynamically, the ants form different subgroups adaptively, and hence the data objects they represent are clustered. Experimental results show that the A4C algorithm on ASM is significantly better than other clustering methods in terms of both speed and quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency.
10441720
Sketch-based change detection: methods, evaluation, and applications
Traffic anomalies such as failures and attacks are commonplace in today's network, and identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows that need to be examined for significant changes in traffic pattern (eg, volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is either too expensive or too slow. We propose building compact summaries of the traffic data using the notion of sketches. We have designed a variant of the sketch data structure, k-ary sketch, which uses a constant, small amount of memory, and has constant per-record update and reconstruction cost. Its linearity property enables us to summarize traffic at various levels. We then implement a variety of time series forecast models (ARIMA, Holt-Winters, etc.) on top of such summaries and detect significant changes by looking for flows with large forecast errors. We also present heuristics for automatically configuring the model parameters.Using a large amount of real Internet traffic data from an operational tier-1 ISP, we demonstrate that our sketch-based change detection method is highly accurate, and can be implemented at low computation and memory costs. Our preliminary results are promising and hint at the possibility of using our method as a building block for network anomaly detection and traffic measurement.
17553402
Efficient Parallel Algorithms for Euclidean Distance Transform
The Euclidean Distance Transform (EDT) converts a binary image into one where each pixel has a value equal to its distance to the nearest foreground pixel. Two parallel algorithms for EDT transform on linear array with reconfigurable pipeline bus system (LARPBS) are presented. For an image with n×n pixels, the first algorithm can complete EDT transform in ) log log log log log log ( n n n O time using n processors. The second algorithm can compute the EDT in O( log n loglog n) time using n n log log 2 processors.
10299300
On the development of a fully automated cryosection image registration system
The CCD cryosection images for the male collected in the National Library of Medicine's Visible Human Project were registered through a procedure involving significant human intervention. In addition, several large misalignments were visible in the registered CCD data for the male. To expedite the exploitation of the visible human cryosection data sets, there is a need to develop a highly accurate yet efficient image registration tool. In this paper, we present the current status of the development of such a fully automated registration system. This system is based on the maximization of mutual information criterion. One key issue of this approach is a robust optimization scheme. The method developed in this paper is to estimate the initial search point through segmentations of regions of interest for each cryosection image slice. Experimental results using the 70 mm cryosection image data for the male are presented in this paper.