1407636
A New Statistical Approach to Personal Name Extraction
We propose a new statistical approach to extracting personal names from a corpus. One of the key points of our approach is that it can both automatically learn the characteristics of personal names from a large training corpus and make good use of human empirical knowledge (e.g., Context Free Grammar). Furthermore, our approach also assigns confidence measures to the extracted personal names, compared with traditional simple true/false determination. Another main contribution of this work is that we have applied the personal name extraction technology into a real application, which is a Chinese inputting system and have achieved an approximately 7% error rate reduction for all characters and 30% error rate reduction for personal names.
432736
ReCoM: reinforcement clustering of multi-type interrelated data objects
Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.
459986
A New Statistical Approach To Chinese Pinyin Input
Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
14828051
MRSSA: an iterative algorithm for similarity spreading over interrelated objects
We introduce the Multiple Relationship Similarity Spreading Algorithm (MRSSA) to enhance IR effectiveness. This method has similarity computed in an iterative "spreading" fashion for multiple object types, combining both inter- and intra-object relationships. We demonstrate the value of this approach in the context of the WWW, where the key objects are web pages and queries, Relationships considered are derived from hyperlinks (in- and out-links) and click-through logs.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
17711119
IRC: an iterative reinforcement categorization algorithm for interrelated Web objects
Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships with other types of objects into account. However, focusing on any single aspects of these interrelationships and objects does not fully reveal their true categories. In this paper, we propose a categorization algorithm, the iterative reinforcement categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web. IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the Fl measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.
45057829
Preclassification of handwritten Chinese characters based on basic stroke substructures
A method for preclassification of handwritten Chinese characters is presented. A set of basic stroke substructures is defined using the consistent stroke connection relations. A knowledge guided recognition process is employed to identify the types of the extracted basic stroke substructures found in a handwritten character. Then a 1-D character coding scheme is given to represent the character and the code can be also used for character preclassification.
13035041
Searching the Web with Queries
In this paper we study the problem of searching the Web with online learning algorithms. We consider that Web documents can be represented by vectors of n boolean attributes. A search engine is viewed as a learner, and a user is viewed as a teacher. We investigate the number of queries a search engine needs from the user to search for a collection of Web documents. We design several efficient learning algorithms to search for any collection of documents represented by a disjunction (or a conjunction) of relevant attributes with the help of membership queries or equivalence queries.
52859343
Towards rate-distortion tradeoff in real-time color video coding
In this paper, we address to the key problem in real-time video coding, the rate-distortion tradeoff. We found that the distribution of the integer transform coefficients in H.264 color video coding can be approximated by Laplacian distribution but the rate-distortion characteristics should be modelled for the luminance and chrominance components separately. So we propose separable rate-distortion models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel rate distortion tradeoff controller is employed. Simulations results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow.
46287200
A Parallel Algorithm for Finding a Triconnected Component Separator with an Application
62617805
Improved rate control for MPEG-4 video transport over wireless channel
Video streaming over wireless channel is challenging due to a number of factors such as limited bandwidth and loss sensitivity. In this paper, we develop a novel rate control algorithm for MPEG-4 video coding. Unlike traditional rate control schemes, we jointly consider the encoding complexity variation and buffer variation as well as human visual properties to optimize the rate control efficiency. We also analyze the sensitivity of a macroblock (MB) as a result of bit errors and calculate its error sensitivity metric. This metric is used in unequal error protection (UEP) of the MB. Simulation results show that proposed approach can improve the decoded picture quality in wireless video coding and transmission.