LCC-DCU C-C question answering task at NTCIR-5

This paper describes the work for our participation in the NTCIR-5 Chinese to Chinese Question Answering task. Our strategy is based on the “Retrieval plus Extraction” approach. We first retrieve relevant documents, then retrieve short passages from the above documents, and finally extract named entity answers from the most relevant passages. For question type identification, we use simple heuristic rules which can cover most questions. The Lemur toolkit with the OKAPI model is used for document retrieval. Results of our task submission are given and some preliminary conclusions drawn

Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering

ICT-DCU question answering task at NTCIR-6

This paper describes details of our participation in the NTCIR-6 Chinese-to-Chinese Question Answering task. We use the “retrieval plus extraction approach” to get answers for questions. We first split the documents into short passages, and then retrieve potentially relevant passages for a question, and finally extract named entity answers from the most relevant passages. For question type identification, we use simple heuristic rules which cover most questions. The Lemur toolkit was used with the okapi model for document retrieval. Results of our task submission are given and some preliminary conclusions drawn

Shaping of molecular weight distribution using b-spline based predictive probability density function control

Issues of modelling and control of molecular weight distributions (MWDs) of polymerization products have been studied under the recently developed framework of stochastic distribution control, where the purpose is to design the required control inputs that can effectively shape the output probability density functions (PDFs) of the dynamic stochastic systems. The B-spline Neural Network has been implemented to approximate the function of MWDs provided by the mechanism model, based on which a new predictive PDF control strategy has been developed. A simulation study of MWD control of a pilot-plant styrene polymerization process has been given to demonstrate the effectiveness of the algorithms

An analysis of question processing of English and Chinese for the NTCIR 5 cross-language question answering task

An important element in question answering systems is the analysis and interpretation of questions. Using the NTCIR 5 Cross-Language Question Answering (CLQA) question test set we demonstrate that the accuracy of deep question analysis is dependent on the quantity and suitability of the available linguistic resources. We further demonstrate that applying question analysis tools developed on monolingual training materials to questions translated Chinese-English and English-Chinese using machine translation produces much reduced effectiveness in interpretation of the question. This latter result indicates that question analysis for CLQA should primarily be conducted in the question language prior to translation

Sensitivity analysis and experimental design of a stiff signal transduction pathway model

Sensitivity analysis is normally used to analyze how sensitive a system is with respect to the change of parameters or initial conditions and is perhaps best known in systems biology via the formalism of metabolic control analysis [1, 2]. The nuclear factor B (NF-B) signalling pathway is an important cellular signalling pathway, of which protein phosphorylation is a major factor controlling the activation of further downstream events. The NF-κB proteins regulate numerous genes that play important roles in inter- and intra-cellular signalling, cellular stress responses, cell growth, survival, and apoptosis. As such, its specificity and its role in the temporal control of gene expression are of crucial physiological interest

A study on mutual information-based feature selection for text categorization

Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholding (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Many existing experiments show IG is one of the most effective methods, by contrast, MI has been demonstrated to have relatively poor performance. According to one existing MI method, the mutual information of a category c and a term t can be negative, which is in conflict with the definition of MI derived from information theory where it is always non-negative. We show that the form of MI used in TC is not derived correctly from information theory. There are two different MI based feature selection criteria which are referred to as MI in the TC literature. Actually, one of them should correctly be termed "pointwise mutual information" (PMI). In this paper, we clarify the terminological confusion surrounding the notion of "mutual information" in TC, and detail an MI method derived correctly from information theory. Experiments with the Reuters-21578 collection and OHSUMED collection show that the corrected MI method’s performance is similar to that of IG, and it is considerably better than PMI

3D DEM Simulation of Crushable Granular Soils under Plane Strain Compression Condition

AbstractParticle crushing plays an important role on the mechanical behavior of crushable granular soils. In this paper, the macro- and micro-mechanical behaviors of dense granular soils composed of crushable agglomerates in plane strain compression test are investigated using the Discrete Element Method (DEM). A detailed study on the effects of particle crushing on the soil behavior is facilitated by a comparison between the simulation results of crushable and uncrushable specimens. The DEM results show a strong dependency of particle crushing on the confining stress level. It is found that under low confining stresses, particle crushing is insignificant and does not affect the shear band formation, which is the primary failure mode in an uncrushable specimen. However, under high confining stresses, significant particle crushing occurs and leads to considerable volumetric compression and reduction of the peak shear stress ratio. More importantly, particle crushing interferes with the formation of shear band and results in massive contractive zones in the specimen, which essentially controls the shear strength behavior

3D DEM Simulation of Crushable Granular Soils under Plane Strain Compression Condition

AbstractParticle crushing plays an important role on the mechanical behavior of crushable granular soils. In this paper, the macro- and micro-mechanical behaviors of dense granular soils composed of crushable agglomerates in plane strain compression test are investigated using the Discrete Element Method (DEM). A detailed study on the effects of particle crushing on the soil behavior is facilitated by a comparison between the simulation results of crushable and uncrushable specimens. The DEM results show a strong dependency of particle crushing on the confining stress level. It is found that under low confining stresses, particle crushing is insignificant and does not affect the shear band formation, which is the primary failure mode in an uncrushable specimen. However, under high confining stresses, significant particle crushing occurs and leads to considerable volumetric compression and reduction of the peak shear stress ratio. More importantly, particle crushing interferes with the formation of shear band and results in massive contractive zones in the specimen, which essentially controls the shear strength behavior