26966022
Term Committee Based Event Identification within News Topics
Most previous research focus on organizing news set into flat collections of stories. However, a topic in news is more than a mere collection of stories: it is characterized by a definite structure of inter-related events. Stories within a topic usually share some terms which are related to the topic other than a specific event, so stories of different events are usually very similar to each other within a topic. To deal with this problem, we propose a new event identification method based on the term committee. We first capture some tight term clusters as term committees of potential events, and then use them to reweight the key terms in a story. The experimental results on two Linguistic Data Consortium (LDC) datasets show that the proposed method for event identification outperforms previous methods significantly.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
18105952
Adapting Prime Number Labeling Scheme for Directed Acyclic Graphs
Directed Acyclic Graph(DAG) could be used for modeling subsumption hierarchies. Several labeling schemes have been proposed or tailored for indexing DAG in order to efficiently explore relationships in such hierarchy. However few of them can satisfy all the requirements in response time, space, and effect of updates simultaneously. In this paper, the prime number labeling scheme is extended for DAG. The scheme invests intrinsic mapping between integer divisibility and subsumption hierarchy, which simplifies the transitive closure computations and diminishes storage redundancy, as well as inherits the dynamic labeling ability from original scheme. Performance is further improved by introducing some optimization techniques. Our extensive experimental results show that prime number labeling scheme for DAG outperforms interval-based and prefix-based labeling schemes in most cases.
4342705
Weighted Ontology-Based Search Exploiting Semantic Similarity
This paper is concerned with the problem of semantic search. By semantic search, we mean searching for instances from knowledge base. Given a query, we are to retrieve ‘relevant’ instances, including those that contain the query keywords and those that do not contain the keywords. This is contrast to the traditional approaches of generating a ranked list of documents that contain the keywords. Specifically, we first employ keyword based search method to retrieve instances for a query; then a proposed method of semantic feedback is performed to refine the search results; and then we conduct re-retrieval by making use of relations and instance similarities. To make the search more effective, we use weighted ontology as the underlying data model in which importances are assigned to different concepts and relations. As far as we know, exploiting instance similarities in search on weighted ontology has not been investigated previously. For the task of instance similarity calculation, we exploit both concept hierarchy and properties. We applied our methods to a software domain. Empirical evaluation indicates that the proposed methods can improve the search performance significantly.
17724018
Logical structure based semantic relationship extraction from semi-structured documents
Addressed in this paper is the issue of semantic relationship extraction from semi-structured documents. Many research efforts have been made so far on the semantic information extraction. However, much of the previous work focuses on detecting `isolated' semantic information by making use of linguistic analysis or linkage information in web pages and limited research has been done on extracting semantic relationship from the semi-structured documents. In this paper, we propose a method for semantic relationship extraction by using the logical information in the semi-structured document (semi-structured document usually has various types of structure information, e.g. a semi-structured document may be hierarchical laid out). To the best of our knowledge, extracting semantic relationships by using logical information has not been investigated previously. A probabilistic approach has been proposed in the paper. Features used in the probabilistic model have been defined.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
38683441
A Wordnet-Based Approach to Feature Selection in Text Categorization
This paper proposes a new feature selection method for text categorization. In this method, word tendency, which takes related words into consideration, is used to select best terms. Our experiments on binary classification tasks show that our method achieves better than DF and IG when the classes are semantically discriminative. Furthermore, our best performance is usually achieved in fewer features.
38952395
Efficient Contour Detection Based On Improved Snake Model
Active contour model, also called snake, adapts to edges in an image. A snake is defined as an energy minimizing spline – the snake's energy depends on its shape and location within the image. Problems associated with initialization and poor convergence to boundary concavities, however, have limited its utility. In this paper, we present a new external force field, named gravitation force field, for the snake model. We associate this force field with edge preserving smoothing to drive the snake for solving the problems. Our gravitation force field uses gradient values as particles to construct force field in the whole image. This force field will attract the active contour toward the edge boundary. The locations of the initial contour are very flexible, such that they can be very far away from the objects and can be inside, outside, or the mixture. The improved snake can converge toward the object boundary in a fast pace.
31414473
Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System
This paper proposes an improved learning algorithm based on generalized SOM for dynamical non-linear system identification. To improve the convergent speed and the accuracy of SOM algorithm, we propose the improved self-organizing algorithm, which, at first, applies the multiple local models instead of the global model, and secondly, adjusts the weights of the computing output layers along with the weights of the competing neurons layer during the training process. We prove that the improved algorithm is convergent if the network has suitable initial weights and small positive real parameters. The simulation results using our improved generalized SOM show an improvement for non-linear system compared to traditional neural network control systems.