8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
1690812
Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that classifies CST relationships between sentence pairs extracted from topically related documents, exploiting both labeled and unlabeled data. We investigate a binary classifier for determining existence of structural relationships and a full classifier using the full taxonomy of relationships. We show that in both cases the exploitation of unlabeled data helps improve the performance of learned classifiers.
13300239
Mining Inter-Entity Semantic Relations Using Improved Transductive Learning
This paper studies the problem of mining relational data hidden in natural language text. In particular, it approaches the relation classification problem with the strategy of transductive learning. Different algorithms are presented and empirically evaluated on the ACE corpus. We show that transductive learners exploiting various lexical and syntactic features can achieve promising classification performance. More importantly, transductive learning performance can be significantly improved by using an induced similarity function.
16080297
Combining multiple clusterings by soft correspondence
Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. We present a new framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, we propose a novel algorithm that iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations also demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.
76259
Service Oriented Internet
Effective service delivery capabilities are critical to the transformation of the Internet into a viable commercial infrastructure. At the present time, the architecture of the Internet is inadequately equipped to provide these capabilities. Traditionally, overlay networks have been proposed as a means of providing rich functionality at the upper layers. However, they suffer from their own drawbacks and do not constitute a perfect solution. In this paper, we propose a novel, overlay based Service Oriented Internet architecture that is meant to serve as a flexible, unifying and scalable platform for delivering services over the Internet. As part of this architecture, we introduce a new two-level addressing scheme and an associated service layer. We also describe the functionality of the new network elements that are introduced, namely service gateway and service point-of-presence, and subsequently discuss algorithms that are responsible for distributing service reachability across the overlay framework. We also present a few examples of application services that benefit significantly when deployed on our architecture.
2845021
Incremental motion estimation through modified bundle adjustment
An incremental motion estimation scheme for long image sequence analysis is introduced. It applies to a sliding window of triplet images and maintains local motion consistency without resort to post-concatenation. This is possible thanks to our newly developed local process called three-view partial bundle adjustment. Unlike previous approaches that rely only on point matches across three or more views, our local process also takes into account all available two-view matches, leading to more accurate motion estimation. For sparse image sequences, two-view matches are very reliable and it becomes even more important to use them since the number of matches across more views decreases quickly. In this case, our incremental method can produce results very close to those obtained with a global bundle adjustment but in a fraction of time. Experiments with both synthetic and real data have been conducted to compare the proposed technique with other techniques, and have shown our technique to be clearly superior.
5414084
Architecture and synthesis for multi-cycle on-chip communication
There are two important infection points in the development of deep submicron (DSM) process technologies. The first point is when the average interconnect delay exceeds the gate delay, which happened during mid 1990s and led to the so-called timing closure problem. The second point is when single-cycle full chip synchronization is no longer possible, which is about to happen soon. It can be shown that, even with the aggressive interconnect optimization techniques (e.g., buffer insertion and wire-sizing), 5 clock cycles are still needed to go from corner-to-corner for the die of 28.3 mm /spl times/ 28.3 mm in the 0.07 /spl mu/m technology generation, assuming a 5.63 GHz clock by 2006 predicted in ITRS'01 (2001). This clearly suggests that multi-cycle on-chip communication is a necessity in multi-gigahertz synchronous designs. However, it is not supported in the current design tools and methodologies, as most of these implicitly assume that full chip synchronization in a single clock cycle is feasible. Our contributions are as follows: (i) we propose a regular distributed register (RDR) microarchitecture which offers high regularity and direct support of multi-cycle communication; (ii) we develop a set of novel architectural synthesis algorithms to efficiently synthesize behavior-level designs onto the RDR architecture.
35739145
A Flexible Algorithm for Extracting Periodic Signals
In this paper, we propose a flexible two-stage algorithm for extracting desired periodic signals. In the first stage, if the period and phase information of the desired signal is available (or can be estimated), a minimum mean square error approach is used to coarsely recover the desired source signal. If only the period information is available (or can be estimated), a robust correlation based method is proposed to achieve the same goal. The second stage uses a higher-order statistics based Newton-like algorithm, derived from a constrained maximum likelihood criteria, to process the extracted noisy signal as cleanly as possible. A parameterized nonlinearity is adopted in this stage, adapted according to the estimated statistics of the desired signal. Compared with many existing extraction algorithms, the proposed algorithm has better performance, which is confirmed by simulations.