690242
Improving Quality of Email Categorization with Tournament Methods
The tournament text classification methods are proposed in this article to perform the task of email categorization, in which the essence is to break down the multi-class categorization process into a set of binary classification tasks. We implement the methods of elimination and Round Robin tournament to classify emails within 15 folders. Substantial experiments are conducted to compare the effectiveness and robustness of the tournament methods against the n-way classification method and the voting method. The experimental results show that the tournament methods outperform the n-way method by 11.7% and the voting method by 4.1% regarding precision, and the Round Robin performs slightly better than the Elimination tournament on average.
45209699
A Workflow Model for Chinese Business Processes
This paper proposes a new workflow model (MAWM) targeted at Chinese business processing. The cultural dependent characteristics of Chinese business processes are identified and taken into account in the MAWM model. MAWM adopts a decentralized modeling style based on the conventional business perspective on labor division and cooperation. This idea is reflected in the concept of agent-workflow. Activity-based and communication-based modeling methods are integrated in the process model. They are used to model different types of business activities. In addition, the model is formalized using Process Algebra and a simplified algorithm is proposed for safety verification of workflow processes.
35278447
An Investigation on Transformation-based Error-driven Learning Algorithm for Chinese Noun Phrase Extraction
Noun phrases are commonly used for generating index terms for information retrieval systems. Therefore, we need an effective noun phrase extraction method. In this paper, we propose an approach to extract maximal noun phrases from Chinese text. Although previous studies have been proposed to extract noun phrases, most of them are only applicable to Western languages. To the best of our knowledge, very few has handled Chinese text. Many existing approaches for Western languages made use of statistical methods. However, due to the complicated structure of maximal Chinese noun phrase, pure statistical approaches are not effective. We attempt to improve the performance of a statistical method by integrating it with the transformation-based error-driven learning (TEL) technique. Our methodology includes two modules. The first module applies a statistical method to extract Chinese noun phrases. The performance of this approach, in terms of precision and recall, is investigated. The second module applies the TEL algorithm to further refine the output of the first module. The TEL algorithm automatically learns a set of transformation rules to fix the errors that are obtained through comparing the output of the first module with the correctly annotated corpus. The learned rules can be applied to sentences in any corpus one by one to correct the errors. The TEL algorithm is shown to be effective in improving the precision.
16840924
Improving Transliteration with Precise Alignment of Phoneme Chunks and Using Contextual Features
Automatic transliteration of foreign names is basically regarded as a diminutive clone of the machine translation (MT) problem. It thus follows IBM's conventional MT models under the source-channel framework. Nonetheless, some parameters of this model dealing with zero-fertility words in the target sequences, can negatively impact transliteration effectiveness because of the inevitable inverted conditional probability estimation. Instead of source-channel, this paper presents a direct probabilistic transliteration model using contextual features of phonemes with a tailored alignment scheme for phoneme chunks. Experiments demonstrate superior performance over the source-channel for the task of English-Chinese transliteration.
7493579
CLARE—a Prolog database machine
The CLAuse Retrieval Engine (CLARE) is a coprocessor system which based on two-stage filtering, for handling large sets of disc resident clauses in Prolog database applications. The overall architecture and the timing measurements of the first stage CLARE hardware are reported. The timing measurements shows that the first stage CLARE hardware can perform search operations on data transferring at a rate up to 4.5 million double bytes per second.
1165688
A Chinese dictionary construction algorithm for information retrieval
In this article we propose a method for constructing, from raw Chinese text, a statistics-based automatic dictionary. The method makes use of local statistical information (i.e., data within a document) to identify and discard repeated string patterns, which, at an earlier stage, were substrings of legitimate words. Global statistical information (which exists throughout the entire corpus) and contextual constraints are then used for further filtering. The method can be used to alleviate the out-of-vocabulary (OOV) problem, which is commonly found in dictionary-based natural language information-processing applications, e.g., word segmentation. It can handle text corpora dynamically and, further, it does not impose any strict requirements on the size and quality of the training corpora. Based on our method, we constructed Chinese dictionaries from different Chinese corpora. We then applied the words in the constructed dictionaries to indexing in information retrieval (IR). Retrieval performance using such indexes was compared to the same, but based on indexes produced by static dictionaries. Three Chinese corpora using various character-encoding schemes and language styles were used in the experiments. The results show that retrieval using indexes based on the constructed dictionary is effective. This implies that fully automatic Chinese dictionary construction based on dynamic data sources, e.g., from the Internet, for the purposes of IR is feasible. Drawing on the experiment, we were able to make some interesting observations: (1) using only a portion of a dictionary is enough to produce good retrieval performance, e.g., a dictionary consisting of only the 500 highest-frequency strings extracted from the NTCIR 2 Chinese corpus produced as good a retrieval result as using a more complete dictionary with over 100K entries; and (2) complete word segmentation is not a strict requirement for achieving practical information retrieval.
42199214
A Technique for Improving the Convergence Characteristic of Genetic Algorithms and Its Application to a Genetic-Based Load Flow Algorithm
This paper is devoted to the development of a technique for the enhancement of the convergence of genetic algorithms. Based on the concept of solution acceleration, a technique is proposed and applied to a constrained-genetic-algorithm load-flow algorithm CGALF recently developed for solving the problem of evaluating the voltage profile and power flow in electric power networks. The enhanced CGALF algorithm is applied to a practical power system to illustrate the effectiveness of the developed method.
10398473
Structural Redocumentation: A Case Study
Most software documentation typically describes the program at the algorithm and data-structure level. For large legacy systems, understanding the system's architecture is more important. The authors propose a method of reverse engineering through redocumentation that promises to extend the useful life of large systems. >
31248498
A pruning method for the recursive least squared algorithm
The recursive least squared (RLS) algorithm is an effective online training method for neural networks. However, its conjunctions with weight decay and pruning have not been well studied. This paper elucidates how generalization ability can be improved by selecting an appropriate initial value of the error covariance matrix in the RLS algorithm. Moreover, how the pruning of neural networks can be benefited by using the final value of the error covariance matrix will also be investigated. Our study found that the RLS algorithm is implicitly a weight decay method, where the weight decay effect is controlled by the initial value of the error covariance matrix; and that the inverse of the error covariance matrix is approximately equal to the Hessian matrix of the network being trained. We propose that neural networks are first trained by the RLS algorithm and then some unimportant weights are removed based on the approximate Hessian matrix. Simulation results show that our approach is an effective training and pruning method for neural networks.
188431
A fast and robust simultaneous pose tracking and structure recovery algorithm for augmented reality applications
A robust simultaneous pose tracking and structure recovery algorithm based on the Interacting Multiple Model (IMM) for augmented reality applications is proposed in this paper. A set of three extended Kalman filters (EKFs), each describes a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of an object. Another set of EKFs, one filter for each model point, is used to refine the positions of the model features in the 3D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.
17549193
Accurate 3-D Motion Tracking with an Application to Super-Resolution
Many of the existing image processing applications, in particular the construction of super-resolution videos, require an accurate high-speed motion tracking algorithm. This paper proposes an efficient recursive approach that recovers 3D motion from a stereo image sequence with high precision based on the trifocal tensor. In the computation process, neither the 3D structure of the scene nor its reconstruction is necessary. The validity of the proposed algorithm is demonstrated by applying it to upgrade the resolution of real stereo images. Empirical comparisons show that our novel approach outperformed traditional work in terms of both the accuracy of motion estimation and the quality of super-resolved images