Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Arthur B. Markman. Vita. Includes bibliographical references.

In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. I continue to combine graph alignment features with lexical semantic similarity measures and employ machine learning techniques to show that grade assignment error can be reduced compared to a system that considers only lexical semantic measures of similarity. I also detail a preliminary attempt to align the dependency graphs of student and instructor answers in order to utilize a structural component that is necessary to simulate human-level grading of student answers. I further explore the utility of these techniques to several related tasks in natural language processing including the detection of text similarity, paraphrase, and textual entailment.

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Over the past several years, new DNA sequencing technologies have led to a great in- crease in the quantity of biological sequence data that can be generated. Typically there may be millions or even billions of short reads sequences of a few hundred base pairs that are to some degree redundant: the data fall naturally into clusters of sequences that are highly similar to each other. In order to reduce the time required for analysis of the data, it therefore becomes of interest to compute representatives of these clusters, based on some definition of similarity. In this thesis we examine two clustering software packages, USEARCH and DNACLUST, that seek to perform this clustering task efficiently. We provide an overview of the techniques used by these two packages; we compare and evaluate them both from a methodological and experimental perspective, and draw conclusions about their effectiveness and utility. / Thesis / Master of Science (MSc)

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In using coal derived syngas in a gas turbine, solid particulates coming out of the gasifier can prove to be detrimental to the engine hardware. Not much is known about particle deposition, erosion, and corrosion on turbine blades as a result of these contaminants. Performing deposition studies at engine like conditions can be difficult. This study presents a method for substituting the particles with polymer materials so studies can be done under more workable conditions. PVC and Teflon particles were used and deposited against a flat plate to mimic a published experiment that used real coal ash. Temperatures were near the melting point of the material and oncoming momentum Stokes numbers were matched. It was found that using polymer materials is not a perfect substitute, but has the same trends and behaves in a similar fashion. PVC particles were then used in an experiment to impact a leading edge with film cooling. The same particle substitution method was used. It was found that increasing the free stream temperature increased the amount of deposition while increasing the blowing ratio slightly decreased deposition. Particle deposition on the leading edge tended to cause an increase in the film cooling effectiveness. It was also found that deposition on the surface slightly increased the convective heat transfer. / Master of Science

BACKGROUND:Metagenomics has a great potential to discover previously unattainable information about microbial communities. An important prerequisite for such discoveries is to accurately estimate the composition of microbial communities. Most of prevalent homology-based approaches utilize solely the results of an alignment tool such as BLAST, limiting their estimation accuracy to high ranks of the taxonomy tree.RESULTS:We developed a new homology-based approach called Taxonomic Analysis by Elimination and Correction (TAEC), which utilizes the similarity in the genomic sequence in addition to the result of an alignment tool. The proposed method is comprehensively tested on various simulated benchmark datasets of diverse complexity of microbial structure. Compared with other available methods designed for estimating taxonomic composition at a relatively low taxonomic rank, TAEC demonstrates greater accuracy in quantification of genomes in a given microbial sample. We also applied TAEC on two real metagenomic datasets, oral cavity dataset and Crohn's disease dataset. Our results, while agreeing with previous findings at higher ranks of the taxonomy tree, provide accurate estimation of taxonomic compositions at the species/strain level, narrowing down which species/strains need more attention in the study of oral cavity and the Crohn's disease.CONCLUSIONS:By taking account of the similarity in the genomic sequence TAEC outperforms other available tools in estimating taxonomic composition at a very low rank, especially when closely related species/strains exist in a metagenomic sample.

Online scientific publications are becoming more and more popular. The number of publications we can access almost instantaneously is rapidly increasing. This makes it more challenging for researchers to pursue a topic, review literature, track research history or follow research trends. Using online resources such as search engines and digital libraries is helpful to find scientific publications, however most of the time the user ends up with an overwhelming amount of linear results to go through. This thesis proposes an alternative system, which takes advantage of citation/reference relations between publications. This demonstrates better insight of the hierarchy distribution of publications around a given topic. We also utilize information visualization techniques to represent the publications as a network. Our system is designed to automatically retrieve publications from Google Scholar and visualize them as a 2-dimensional graph representation using the citation relations. In this, the nodes represent the documents while the links represent the citation/reference relations between them. Our visualization system provides a better view of publications, making it easier to identify the research flow, connect publications, and assess similarities/differences between them. It is an interactive web based system, which allows the users to get more information about any selected publication and calculate a similarity score between two selected publications. Traditionally, similar documents are found using Natural Language Processing (NLP), which compares documents based on matching their contents. In the proposed method, similar documents are found using the citation/reference relations which are iii represented by the relationship that was originally inputted by the authors. We propose a new path based metric for measuring the similarity scores between any pair of publications. This is based on both the number of paths and the length of each path. More paths and shorter lengths increase the similarity score. We compare our similarity score results with another similarity score from Scurtu’s Document Similarity [1] that uses the NLP method. We then use the average of the similarity scores collected from 15 users as a ground truth to validate the efficiency of our method. The results indicate that our Citation Network approach yielded better scores than Scurtu’s approach.

Yes / Real-world datasets are increasingly heterogeneous, showing a mixture of numerical, categorical and other feature types. The main challenge for mining heterogeneous datasets is how to deal with heterogeneity present in the dataset records. Although some existing classifiers (such as decision trees) can handle heterogeneous data in specific circumstances, the performance of such models may be still improved, because heterogeneity involves specific adjustments to similarity measurements and calculations. Moreover, heterogeneous data is still treated inconsistently and in ad-hoc manner. In this paper, we study the problem of heterogeneous data classification: our purpose is to use heterogeneity as a positive feature of the data classification effort by using consistently the similarity between data objects. We address the heterogeneity issue by studying the impact of mixing data types in the calculation of data objects’ similarity. To reach our goal, we propose an algorithm to divide the initial data records based on pairwise similarity for classification subtasks with the aim to increase the quality of the data subsets and apply specialized classifier models on them. The performance of the proposed approach is evaluated on 10 publicly available heterogeneous data sets. The results show that the models achieve better performance for heterogeneous datasets when using the proposed similarity process.