10164763
Automatic Pattern Construction For Web Information Extraction
This paper describes a domain independent approach for automatically constructing information extraction patterns for semi-structured web pages. Given a randomly chosen page from a web site of similarly structured pages, the system identifies a region of the page that has a regular "tabular" structure, and then infers an extraction pattern that will match the "rows" of the region and identify the data elements. The approach was tested on three corpora containing a series of tabular web sites from different domains and achieved a success rate of at least 80%. A significant strength of the system is that it can infer extraction patterns from a single training page and does not require any manual labeling of the training page.
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.
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.
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.
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.
1036423
Investigation of Brood Size in GP with Brood Recombination Crossover for Object Recognition
This paper describes an approach to the investigation of brood size in the brood recombination crossover method in genetic programming for object recognition problems. The approach is examined and compared with the standard crossover operator on three object classification problems of increasing difficulty. The results suggest that the brood recombination method outperforms the standard crossover operator for all the problems in terms of the classification accuracy. As the brood size increases, the system effective performance can be improved. When it exceeds a certain point, however, the effective performance will not be improved and the system will become less efficient.
19644682
A New Cluster Validity Function Based on the Modified Partition Fuzzy Degree
The cluster validity is an important topic of cluster analysis, which is often converted into the determination of the optimal cluster number. Most of the available cluster validity functions are limited for the analysis of numeric data set and ineffective for the categorical data set. For this purpose, a new cluster validity function is presented in this paper, namely the modified partition fuzzy degree. By combining the partition entropy and the partition fuzzy degree, the new cluster validity can be applied to any data set with numeric attributes or categorical attributes. The experimental results illustrate the effectiveness of the proposed cluster validity function.
13928844
Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing
News story parsing is an important and challenging task in a news video library system. We address two important components in a news video story parsing system: shot boundary detection and anchorperson detection. First, an unsupervised fuzzy c-means algorithm is used to detect video-shot boundaries in order to segment a news video into video shots. Then, a graph-theoretical cluster analysis algorithm is implemented to classify the video shots into anchorperson shots and news footage shots. Because of its unsupervised nature, the algorithms require little human intervention. The efficacy of the proposed method is extensively tested on more than five hours of news programs.
28547876
Extraction of Sagittal Symmetry Planes from Pet Images
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.
7220368
Quantification and characterisation of arteries in retinal images
A computerised system is presented for the automatic quantification of blood vessel topography in retinal images. This system utilises digital image processing techniques to provide more reliable and comprehensive information for the retinal vascular network. It applies strategies and algorithms for measuring vascular trees and includes methods for locating the centre of a bifurcation, detecting vessel branches, estimating vessel diameter, and calculating angular geometry at a bifurcation. The performance of the system is studied by comparison with manual measurements and by comparing measurements between red-free images and fluorescein images. In general an acceptable degree of accuracy and precision was seen for all measurements, although the system had difficulty dealing with very noisy images and small or especially tortuous blood vessels.