16297828
Neural Networks for Scientific Paper Classification
This paper describes an approach to the use of neural networks for improving the scientific paper classification performance. On the basis of the initial classification results obtained from the content-based naive Bayes method, this approach uses neural networks to model the citation link structures of the scientific papers for refining the class labels of the documents. The approach is examined and compared with the naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that using citation link structures, neural networks can significantly improve the system performance over the content-based naive Bayes method for all the training set sizes
39682007
TreeWrapper: Automatic Data Extraction Based on Tree Representation
This paper introduces a new algorithm that learns to extract data from Web pages with relatively regular data structures. Current existing systems require training on either manually labelled pages or at least two similar unlabelled pages, and they often have difficulties on handling Web pages with complex formats such as nested tables or lists. Our previous system AutoWrapper does not need any training and can automatically extract data from any single page. This paper improves AutoWrapper by handling nested structures and finding multiple regular data areas. The main contributions include a tree-based representation for Web pages, an expressive language for representing information extraction patterns, and a learning algorithm that automatically detects regular data areas by finding similar sub-trees.
40362881
A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites
This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.
1168192
Approximately Repetitive Structure Detection for Wrapper Induction
In recent years, much work has been invested into automatically learning wrappers for information extraction from HTML tables and lists. Our research has focused on a system that can learn a wrapper from a single unlabelled page. An essential step is to locate the tabular data within the page. This is not trivial when the structures of data tuples are similar but not identical. In this paper we describe an algorithm that can automatically detect approximate repetitive structures within one sequence. The algorithm does not rely on any domain knowledge or HTML heuristics and it can be used in detecting repetitive patterns and hence to learn wrappers from a single unlabeled tabular page.
17022495
Improving Web clustering by cluster selection
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
40240913
Combining Contents and Citations for Scientific Document Classification
This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
32284423
Automatic News Audio Classification Based on Selective Ensemble SVMs
With the rapid growing amount of multimedia, content-based information retrieval has become more and more important. As a significant clue for video indexing and retrieval, audio detection and classification attracts much more attention and becomes a hot topic. On the basis of the priori model of news video structure, a selective ensemble support vector machines (SE-SVMs) is proposed to detect and classify the news audio into 4 types, i.e., silence, music, speech, and speech with music background. Experiments with news audio clips of 8514 seconds in total length illustrate that the average accuracy rate of the proposed audio classification method reaches to 98.9%, which is much better than that of the available SVM-based or traditional threshold-based method.
1923404
Global communication optimization for tensor contraction expressions under memory constraints
The accurate modeling of the electronic structure of atoms and molecules involves computationally intensive tensor contractions involving large multi-dimensional arrays. The efficient computation of complex tensor contractions usually requires the generation of temporary intermediate arrays. These intermediates could be extremely large, but they can often be generated and used in batches through appropriate loop fusion transformations. To optimize the performance of such computations on parallel computers, the total amount of inter-processor communication must be minimized, subject to the available memory on each processor In this paper we address the memory-constrained communication minimization problem in the context of this class of computations. Based on a framework that models the relationship between loop fusion and memory usage, we develop an approach to identify the best combination of loop fusion and data partitioning that minimizes inter-processor communication cost without exceeding the per-processor memory limit. The effectiveness of the developed optimization approach is demonstrated on a computation representative of a component used in quantum chemistry suites.
1210426
De-interlacing Algorithm Based on Motion Objects
A novel de-interlacing algorithm based on motion objects is presented in this paper. In this algorithm, natural motion objects, not contrived blocks, are considered as the processing cells, which are accurately detected by a new scheme, and whose matching objects are quickly searched by the immune clonal selection algorithm. This novel algorithm integrates many other de-interlacing methods, so it is more adaptive to various complex video sequences. Moreover, it can perform the motion compensation for objects with the translation, rotation as well as the scaling transform. The experimental results illustrate that compared with the block matching method with full search, the proposed algorithm greatly improve the efficiency and performance.