17742402
A Unicode-based Adaptive Segmenter
This paper presents a Unicode based Chinese word segmentor. It can handle Chinese text in Simplified, Traditional, or mixed mode. The system uses the strategy of divide-and-conquer to handle the recognition of personal names, numbers, time and numerical values, etc in the preprocessing stage. The segmentor further uses tagging information to work on disambiguation. Adopting a modular design approach, different functional parts are separately implemented using different modules and each module tackles one problem at a time providing more flexibility and extensibility. Results show that with added pre-processing modules and accessorial modules, the accuracy of the segmentor is increased and the system is easily adaptive to different applications.
71018
Experimental Study on Representing Units in Chinese Text Categorization
This paper is a comparative study on representing units in Chinese text categorization. Several kinds of representing units, including byte 3-gram, Chinese character, Chinese word, and Chinese word with part of speech tag, were investigated. Empirical evidence shows that when the size of training data is large enough, representations of higher-level or with larger feature spaces result in better performance than those of lower level or with smaller feature spaces, whereas when the training data is limited the conclusion may be the reverse. In general, representations of higher-level or with larger feature spaces need more training data to reach the best performance. But, as to a specific representation, the size of training data and the categorization performance are not always positively correlated.
195606600
CTEMP: a chinese temporal parser for extracting and normalizing temporal information
Temporal information is useful in many NLP applications, such as information extraction, question answering and summarization. In this paper, we present a temporal parser for extracting and normalizing temporal expressions from Chinese texts. An integrated temporal framework is proposed, which includes basic temporal concepts and the classification of temporal expressions. The identification of temporal expressions is fulfilled by powerful chart-parsing based on grammar rules and constraint rules. We evaluated the system on a substantial corpus and obtained promising results.
34801552
Profile-based event tracking
In this research, we focus on tracking topics that originate and evolve from a specific event. Intuitively, a few key elements of a target event, such as date, location, and persons involved, would be enough for making a decision on whether a test story is on-topic. Consequently, a profile-based event tracking method is proposed. We attempt to build an event profile from the given on-topic stories by robust information retrieval technologies. A feature selection metric and a recognized event clause are utilized to determine most (if not all) key semantic elements of the target event. Preliminary experiments on the TDT2 mandarin corpus show that this profile-based event tracking method is promising.
1260337
An Improved k-Nearest Neighbor Algorithm for Text Categorization
k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is uneven. Some classes may have more samples than others. Therefore, the system performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias on large categories. To deal with these problems, we propose an improved kNN algorithm, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories. More samples (nearest neighbors) will be used for deciding whether a test document should be classified to a category, which has more samples in the training set. Preliminary experiments on Chinese text categorization show that our method is less sensitive to the parameter k than the traditional one, and it can properly classify documents belonging to smaller classes with a large k. The method is promising for some cases, where estimating the parameter k via cross-validation is not allowed.
3580064
Bit section instruction set extension of ARM for embedded applications
Programs that manipulate data at subword level, i.e. bit sections within a word, are common place in the embedded domain. Examples of such applications include media processing as well as network processing codes. These applications spend significant amounts of time packing and unpacking narrow width data into memory words. The execution time and memory overhead of packing and unpacking operations can be greatly reduced by providing direct instruction set support for manipulating bit sections.In this paper we present the Bit Section eXtension (BSX) to the ARM instruction set. We selected the ARM processor for this research because it is one of the most popular embedded processor which is also being used as the basis of building many commercial network processing architectures. We present the design of BSX instructions and their encoding into the ARM instruction set. We have incorporated the implementation of BSX into the Simplescalar ARM simulator from Michigan. Results of experiments with programs from various benchmark suites show that by using BSX instructions the total number of instructions executed at runtime by many transformed functions are reduced by 4.26% to 27.27% and their code sizes are reduced by `1.27% to 21.05%.
46149042
Data reconciliation for real-time optimization of an industrial coke-oven-gas purification process
Online optimization is more and more used in the chemical industry to run a process near its optimum operating condition by providing real-time computed optimal set-points to the distributed control system. Process measurements are necessary for these applications to determine the actual state of the process and to increase the accuracy of the model with parameter estimation techniques. However, these measurements usually contain random as well as gross errors which have to be identified and eliminated before the measurements are used for online optimization. In this contribution, a data reconciliation approach was integrated into an online optimization framework for the ammonia hydrogen sulfide circulation scrubbing, a common industrial coke-oven-gas purification process. We used a rigorous rate-based model to describe this reactive absorption and desorption process. To increase the accuracy of the model, we estimated several process parameters using a sequential parameter estimation approach. Data reconciliation was performed based on simple component balances to achieve model-consistent data and to identify measurement biases. The model was then validated online on a pilot plant by connecting the estimation package through the process control system. Based on the online measured data, operating cost minimization was carried out and the computed optimal set-points realized real-time. A satisfactory agreement between measured data and optimization was achieved.
14227223
Bridging the semantic gap in sports video retrieval and summarization
One of the major challenges facing current media management systems and related applications is the so-called ''semantic gap'' between the rich meaning that a user desires and the shallowness of the content descriptions that are automatically extracted from the media. In this paper, we address the problem of bridging this gap in the sports domain. We propose a general framework for indexing and summarizing sports broadcast programs, with a high-level model of sports broadcast video using the concept of an event, defined according to domain-specific knowledge for different types of sports. Within this general framework, we develop automatic event detection algorithms that are based on automatic analysis of the visual and aural signals in the media. We have successfully applied the event detection algorithms to different types of sports including American football, baseball, Japanese sumo wrestling, and soccer. Event modeling and detection contribute to the reduction of the semantic gap by providing rudimentary semantic information obtained through media analysis. We further propose a novel approach, which makes use of independently generated rich textual metadata, to fill the gap completely through synchronization of the information-laden textual data with the basic event segments. We implemented an MPEG-7 compliant browsing system for semantic retrieval and summarization of sports video using the proposed algorithms.
7456112
Efficient and guaranteed service coverage in partitionable mobile ad-hoc networks
In wireless ad-hoc networks, the network topology changes dynamically and unpredictably due to node mobility. Such topological dynamics are further exacerbated by the natural grouping behavior in the mobile user's movement, which leads to frequent network partitioning. Network partitioning poses significant challenges to the provisioning of centralized services in ad-hoc networks, since partitioning disconnects many mobile users from the central server. We propose a collection of novel run-time algorithms that adaptively ensure the centralized service is available to all mobile nodes during network partitioning, while minimizing the number of servers required. The network-wide service coverage is achieved by partition prediction and service replication on the servers, and assisted by distributed service selection on regular mobile nodes. Simulation results show that our algorithm efficiently achieves guaranteed service coverage to all nodes. To the best of our knowledge, there have been no similar approaches that use partition prediction to provision centralized services adaptively in partitionable mobile ad-hoc networks.
3592974
Code Compaction of Matching Single-Entry Multiple-Exit Regions
With the proliferation of embedded devices and systems, there is renewed interest in the generation of compact binaries. Code compaction techniques identify code sequences that repeatedly appear in a program and replace them by a single copy of the recurring sequence. In existing techniques such sequences are typically restricted to single-entry single-exit regions in the control flow graph. We have observed that in many applications recurring code sequences form single-entry multipleexit (SEME) regions. In this paper we propose a generalized algorithm for code compaction that first decomposes a control flow graph into a hierarchy of SEME regions, computes signatures of SEME regions, and then uses the signatures to find pairs of matching SEME regions. Maximal sized matching SEME regions are found and transformed to achieve code compaction. Our transformation is able to compact matching SEME regions whose exits may lead to a combination of identical and differing targets. Our experiments show that this transformation can lead to substantial reduction in code size for many embedded applications.