2010014
Training a selection function for extraction
In this paper we compare performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles in order to learn how topic prominence affects the performance of each heuristic. We study how different query types can affect the performance of each heuristic and discuss the possibility of using machine learning algorithms to automatically learn good combination functions to combine several heuristics. We also briefly describe the design, implementation, and performance of a multilingual text summarization system SUMMARIST.
43748451
The Use of External Knowledge of Factoid QA
This paper describes recent development in the Webclopedia QA (Question Answering) system, focusing on the use of knowledge resources such as WordNet and a QA typology to improve the basic operations of candidate answer retrieval, ranking, and answer matching.
172983
The Effectiveness of Dictionary and Web-Based Answer Reranking
We describe an in-depth study of using a dictionary (WordNet) and web search engines (Altavista, MSN, and Google) to boost the performance of an automated question answering system, Webclopedia, in answering definition questions. The results indicate applying dictionary and web-based answer reranking together increase the performance of Webclopedia on a set of 102 TREC-10 definition questions by 25% in mean reciprocal rank score and 14% in finding answers in the top 5.
322920
Introduction to the special issue on statistical language modeling
The goal of statistical language modeling (SLM) is to estimate the likelihood (or probability) of a word string. SLM is fundamental to many natural language applications like automatic speech recognition (ASR) [Jelinek 1990], statistical machine translation (SMT) [Brown et al. 1993], and Asian language text input [Gao et al. 2002a]. The research on SLM basically involves two main tasks: modeling and estimation. Modeling is to determine the structure of a statistical model; estimation is to determine the free parameters of the model using training data. SLM usually uses a parametric model with Maximum Likelihood Estimation (MLE) and various smoothing methods to tackle data sparseness problems. Different statistical models have been proposed in the past, but n-gram models (in particular, bigram and trigram models) still dominate SLM research. SLM has recently been demonstrated as an effective framework for a few new applications, such as question answering [Berger 2001], text summarization, paraphrasing [Barzilay and Lee 2004], and information retrieval [Croft and Lafferty 2003]. However, these new applications come with new challenges. For example, in the SLM approaches to information retrieval, a language model has to be trained on a single document, an extremely small training set; while in ASR, a language model is typically trained on a million word corpus. The recent development of related techniques stimulates new modeling and estimation methods that are beyond the scope of the traditional approaches. Two representative examples of such techniques are statistical parsing and discriminative training. With the ever-increasing popularity of SLM, we think that it is the right time to assemble a special issue reflecting recent advances in both its theory and applications. It __________________________________________________________________________________________
40804846
Question Answering in Webclopedia
SHAPE ADJECTIVE COLOR DISEASE TEXT NARRATIVE* GENERAL-INFO DEFINITION USE EXPRESSION-ORIGIN HISTORY WHY-FAMOUS BIO ANTECEDENT INFLUENCE CONSEQUENT CAUSE-EFFECT METHOD-MEANS CIRCUMSTANCE-MEANS REASON EVALUATION PRO-CON CONTRAST RATING COUNSEL-ADVICE To create the QA Typology, we analyzed 17,384 questions and their answers (downloaded from answers.com); see (Gerber, 2001). The Typology contains 94 nodes, of which 47 are leaf nodes; a section of it appears in Figure 2. Each Typology node has been annotated with examples and typical patterns of expression of both Question and Answer, as indicated in Figure 3 for Proper-Person. Question examples Question templates Who was Johnny Mathis' high school track coach? who be 's Who was Lincoln's Secretary of State? Who was President of Turkmenistan in 1994? who be of Who is the composer of Eugene Onegin? Who is the CEO of General Electric? Actual answers Answer templates Lou Vasquez, track coach of...and Johnny Mathis , of Signed Saparmurad Turkmenbachy [Niyazov], of president of Turkmenistan ...Turkmenistan’s President Saparmurad Niyazov... ’s ...in Tchaikovsky's Eugene Onegin... 's Mr. Jack Welch, GE chairman... ... ...Chairman John Welch said ...GE's | of related role-verb Figure 3. Portion of QA Typology node annotations for Proper-Person.
15949067
Security authentication of 3G-WLAN interworking
With the development of wireless Internet, the interworking of the third generation (3G) wide area wireless networks and wireless local area networks (WLAN) has become a focus of research. 3G systems have their advantages in charging management, roaming, and security facilities, and WLAN systems have the advantages of high bandwidth and low investment cost. Interworking of 3G-WLAN offers characteristics that complement each other perfectly. Several authentication protocols have been proposed to improve security. In this paper, we firstly introduce the architectures of 3G-WLAN interworking and discuss existing problems related to authentication and key agreement procedures. Then, we present two schemes by applying authentication based on EAP-TLS (extensible authentication protocol-transport layer security) and EAP-TTLS (tunneled TLS) into integrated 3G and WLAN to achieve high end-to-end security and authentication for the users. Finally, we conduct a series of performance evaluation on the energy consumption and latency of standard authentication protocols.
7645082
An annotation language for optimizing software libraries
This paper introduces an annotation language and a compiler that together can customize a library implementation for specific application needs. Our approach is distinguished by its ability to exploit high level, domain-specific information in the customization process. In particular, the annotations provide semantic information that enables our compiler to analyze and optimize library operations as if they were primitives of a domain-specific language. Thus, our approach yields many of the performance benefits of domain-specific languages, without the effort of developing a new compiler for each domain.
This paper presents the annotation language, describes its role in optimization, and illustrates the benefits of the overall approach. Using a partially implemented compiler, we show how our system can significantly improve the performance of two applications written using the PLAPACK parallel linear algebra library.
6373317
QoS-aware Composite Service Selection in Grids
Grid computing powered by Web service technology is creating abundant service-oriented applications which are promoting the sharing and collaboration of global resources. Complex tasks can be fulfilled quickly and efficiently by combining (physically distributed) individual services into a competent composite service. Since a lot of available individual services have the same functional properties while different non-functional properties (e.g. quality of service), decisions have to be made to select superior individual services from candidates for service composition. In this paper, the authors present a quality of service (QoS) model of service composition, as well as a composite service selection approach which can optimally choose the competent services, in the sense of satisfying users QoS requirement, for the demanded composite services with specified composite structures
9009444
A Portable Parallel N-Body Solver
We present parallel solutions for direct and fast n-body solvers written in the ZPL language. We describe the direct solver, compare its performance against a sequential C program, and show performance results for two very different parallel machines; the KSR-2 and the Paragon. We also discuss the implementation of the fast solver in ZPL, including factors pertinent to data movement.
14630306
Fuzzy neural network classification design using support vector machine
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network classification (SVFNNC) is proposed. The SVFNNC combines the superior classification power of support vector machine (SVM) in high reasoning of FNN in handling uncertainty information. The learning algorithm consists of two learning phases. In the phase 1, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the phase 2, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. To investigate the effectiveness of the proposed SVFNNC, it is applied to the iris, vehicle and dna datasets. Experimental results show that the proposed SVFNNC can achieve good classification performance with drastically reduced number of fuzzy kernel functions.