7609831
Learning cross-document structural relationships using boosting
Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.
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.
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.
14083998
Towards CST-enhanced summarization
In this paper, we propose to enhance the process of automatic extractive multi-document text summarization by taking into account cross-document structural relationships as posited in Cross-document Structure Theory (CST). An arbitrary multidocument extract can be CST-enhanced by replacing low-salience sentences with other sentences that increase the total number of CST relationships included in the summary. We show that CST-enhanced summaries outperform their unmodified counterparts using the relative utility evaluation metric. We also show that the effect of a CST relationship on an extract depends on its type.
3159534
FAST: Toward more effective and efficient image retrieval
This paper focuses on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region-based approach to improving the retrieval effectiveness and robustness; (2) development of a new hierarchical indexing structure and the corresponding hierarchical, elimination-based A* retrieval (HEAR) algorithm to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case; (3) employment of user relevance feedback to tailor the effective retrieval to each user's individualized query preference through the novel indexing tree pruning (ITP) and adaptive region weight updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds great promise in delivering fast and semantics-tailored image retrieval in CBIR.
5969857
A data mining approach to modeling relationships among categories in image collection
This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called α-Semantics Graph, to discover the hidden semantic relationships among the semantic categories embodied in the image collection. With the α-Semantics Graph, each semantic category is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and semantic overlap among the categories in the feature space. The model is utilized in the semantics-intensive image retrieval application. An algorithm using the classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and the image retrieval prototype system utilizing the derived model is promising both in effectiveness and efficiency.
39930413
A wavelet to DCT progressive image transcoder
A transcoder design is proposed in which the transcoder loads the pre-encoded embedded wavelet coefficients and computes the DCT coefficients. The resulting DCT coefficients are quantized and sorted by multi-grid embedded coding to output a compressed DCT bitstream. This transcoder has two operational modes: open-loop and closed-loop. The open-loop scheme is designed for direct transcoding, targeted to applications in which receivers are connected to the transcoder through a fast network and demand the same rate. In contrast, the closed-loop scheme is designed for fully progressive transcoding, targeted to receivers which are connected to the transcoder through a slow network connection, and may demand heterogeneous rates. The performance of the transcoder is evaluated in both open-loop mode and close-loop mode.
16882965
The performance of the cedar multistage switching network
While multistage switching networks for vector multiprocessors have been studied extensively, detailed evaluations of their performance are rare. Indeed, analytical models, simulations with pseudo-synthetic loads, studies focused on average-value parameters, and measurements of networks disconnected from the machine, all provide limited information. In this paper, instead, we present an in-depth empirical analysis of a multistage switching network in a realistic setting: we use hardware probes to examine the performance of the omega network of the Cedar shared-memory machine executing real applications. The machine is configured with 16 vector processors.The analysis suggests that the performance of multistage switching networks is limited by traffic non-uniformities. We identify two major non-uniformities that degrade Cedar's performance and are likely to slow down other networks too. The first one is the contention caused by the return messages in a vector access as they converge from the memories to one processor port. This traffic convergence penalizes vector reads and, more importantly, causes tree saturation. The second non-uniformity is the uneven contention delays induced by even a relatively fair scheme to resolve message collisions. Based on our observations, we argue that intuitive optimizations for multistage switching networks may not be cost-effective. Instead, we suggest changes to increase the network bandwidth at the root of the traffic convergence tree and to delay traffic convergence up until the final stages of the network.
37643061
Adjacent strong edge coloring of graphs
Abstract For a graph G ( V , E ), if a proper k -edge coloring ƒ is satisfied with C ( u ) ≠ C ( v ) for uv ∈ E ( G ), where C(u) = {ƒ(uv) | uv ∈ E} , then ƒ is called k-adjacent strong edge coloring of G , is abbreviated k-ASEC , and χ ′ as ( G ) = min { k | k - ASEC of G } is called the adjacent strong edge chromatic number of G . In this paper, we discuss some properties of χ′ as ( G ), and obtain the χ′ as ( G ) of some special graphs and present a conjecture: if G are graphs whose order of each component is at least six, then χ ′ as ( G ) ≤ Δ ( G ) + 2, where Δ( G ) is the maximum degree of G .