Master of Science / Department of Computing and Information Sciences / William H. Hsu / Logistic regression is a model for function estimation that measures the relationship between independent variables and a categorical dependent variable, and by approximating a conditional probabilistic density function using a logistic function, also known as a sigmoidal function. Multinomial logistic regression is used to predict categorical variables where there can be more than two categories or classes. The most common type of algorithm for optimizing the cost function for this model is gradient descent. In this project, I implemented logistic regression using conjugate gradient descent (CGD). I used the 20 Newsgroups data set collected by Ken Lang. I compared the results with those for existing implementations of gradient descent. The conjugate gradient optimization methodology outperforms existing implementations.

In this paper, we present a automatic item classification system,called AICS. This system is according the content structure that are provided from the teacher for create a content tree. This content tree can correlate the item with content. The main works of AICS classify the item and find the most similar content. After than the system compute the relationship between the item and content, AICS can automatic compute the difficulty of item and examination. The work of this research has two categories: 1. The system can show the content that are related to the item and help the teacher understand the difficulty of the examination paper quickly. 2. When after the examination, the system provide the content for student understand the irrelevant items.

Text analysis is a significant branch of natural language processing, and includes manydifferent sub-fields such as topic modeling, document classification, and sentiment analysis.Unsurprisingly, those who do text analysis are concerned with the runtime of their algorithmsSome of these algorithms have runtimes that depend jointly on the size of the corpus beinganalyzed, as well as the size of that corpus's vocabulary. Trivially, a user may reduce theamount of data they feed into their model to speed it up, but we assume that users will behesitant to do this as more data tends to lead to better model quality. On the other hand,when the runtime also depends on the vocabulary of the corpus, a user may instead modifythe vocabulary to attain a faster runtime. Because elements of the vocabulary also add tomodel quality, this puts users into the position of needing to modify the corpus vocabulary inorder to reduce the runtime of their algorithm while maintaining model quality. To this end,we look at the relationship between model quality and runtime for text analysis by looking atthe effect that current techniques in vocabulary reduction have on algorithmic runtime andcomparing that with their effect on model quality. Despite the fact that this is an importantrelationship to investigate, it appears little work has been done in this area. We find thatmost preprocessing methods do not have much of an effect on more modern algorithms, butproper rare word filtering gives the best results in the form of significant runtime reductionstogether with slight improvements in accuracy and a vocabulary size that scales efficiently aswe increase the size of the data.

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Deep learning models for scanned document image classification and form understand- ing have made significant progress in the last few years. High accuracy can be achieved by a model with the help of copious amounts of labelled training data for closed-world classification. However, very little work has been done in the domain of fine-grained and head-tailed(class imbalance with some classes having high numbers of data points and some having a low number of data points) open-world classification for documents. Our proposed method achieves a better classification results than the baseline of the head-tail-novel/open dataset. Our techniques include separating the head-tail classes and transferring the knowledge from head data to the tail data. This transfer of knowledge also improves the capability of recognizing a novel category by 15% as compared to the baseline.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Global epidemic surveillance is an essential task for national biosecurity management and bioterrorism prevention. The main goal is to protect the public from major health threads. To perform this task effectively one requires reliable, timely and accurate medical information from a wide range of sources. Towards this goal, we present a framework for epidemiological analytics that can be used to extract and visualize infectious disease outbreaks from the variety of unstructured web sources automatically. More precisely, in this thesis, we consider several research tasks including document relevance classification, entity extraction and animal disease-related event recognition in the veterinary epidemiology domain. First, we crawl web sources and classify collected documents by topical relevance using supervised learning algorithms. Next, we propose a novel approach for automated ontology construction in the veterinary medicine domain. Our approach is based on semantic relationship discovery using syntactic patterns. We then apply our automatically-constructed ontology for the domain-specific entity extraction task. Moreover, we compare our ontology-based entity extraction results with an alternative sequence labeling approach. We introduce a sequence labeling method for the entity tagging that relies on syntactic feature extraction using a sliding window. Finally, we present our novel sentence-based event recognition approach that includes three main steps: entity extraction of animal diseases, species, locations, dates and the confirmation status n-grams; event-related sentence classification into two categories - suspected or confirmed; automated event tuple generation and aggregation. We show that our document relevance classification results as well as entity extraction and disease-related event recognition results are significantly better compared to the results reported by other animal disease surveillance systems.

Recentemente, houve um aumento rápido na criação e disponibilidade de repositórios de dados, o que foi percebido nas áreas de Mineração de Dados e Aprendizagem de Máquina. Este fato deve-se principalmente à rápida criação de tais dados em redes sociais. Uma grande parte destes dados é feita de texto, e a informação armazenada neles pode descrever desde perfis de usuários a temas comuns em documentos como política, esportes e ciência, informação bastante útil para várias aplicações. Como muitos destes dados são criados em fluxos, é desejável a criação de algoritmos com capacidade de atuar em grande escala e também de forma on-line, já que tarefas como organização e exploração de grandes coleções de dados seriam beneficiadas por eles. Nesta dissertação um modelo probabilístico, on-line e incremental é apresentado, como um esforço em resolver o problema apresentado. O algoritmo possui o nome DV-INBC e é uma extensão ao algoritmo INBC. As duas principais características do DV-INBC são: a necessidade de apenas uma iteração pelos dados de treino para criar um modelo que os represente; não é necessário saber o vocabulário dos dados a priori. Logo, pouco conhecimento sobre o fluxo de dados é necessário. Para avaliar a performance do algoritmo, são apresentados testes usando datasets populares. / Recently the fields of Data Mining and Machine Learning have seen a rapid increase in the creation and availability of data repositories. This is mainly due to its rapid creation in social networks. Also, a large part of those data is made of text documents. The information stored in such texts can range from a description of a user profile to common textual topics such as politics, sports and science, information very useful for many applications. Besides, since many of this data are created in streams, scalable and on-line algorithms are desired, because tasks like organization and exploration of large document collections would be benefited by them. In this thesis an incremental, on-line and probabilistic model for document classification is presented, as an effort of tackling this problem. The algorithm is called DV-INBC and is an extension to the INBC algorithm. The two main characteristics of DV-INBC are: only a single scan over the data is necessary to create a model of it; the data vocabulary need not to be known a priori. Therefore, little knowledge about the data stream is needed. To assess its performance, tests using well known datasets are presented.

O objetivo da Web Semântica é prover a descrição semântica dos recursos através de metadados processáveis por máquinas. Essa camada semântica estende a Web já existente agregando facilidades para a execução de pesquisas, filtragem, resumo ou intercâmbio de conhecimento de maior complexidade. Dentro deste contexto, as bibliotecas digitais são as aplicações que estão iniciando o processo de agregar anotações semânticas às informações disponíveis na Web. Uma biblioteca digital pode ser definida como uma coleção de recursos digitais selecionados segundo critérios determinados, com alguma organização lógica e de modo acessível para recuperação distribuída em rede. Para facilitar o processo de recuperação são utilizados metadados para descrever o conteúdo armazenado. Porém, a geração manual de metadados é uma tarefa complexa e que demanda tempo, além de sujeita a falhas. Portanto a extração automática ou semi-automática desses metadados seria de grande ajuda para os autores, subtraindo uma tarefa do processo de publicação de documentos. A pesquisa realizada nesta dissertação visou abordar esse problema, desenvolvendo um extrator de metadados que popula uma ontologia de documentos e classifica o documento segundo uma hierarquia pré-definida. A ontologia de documentos OntoDoc foi criada para armazenar e disponibilizar os metadados extraídos, assim como a classificação obtida para o documento. A implementação realizada focou-se em artigos científicos de Ciência da Computação e utilizou a classificação das áreas da ACM na tarefa de classificação dos documentos. Um conjunto de exemplos retirados da Biblioteca Digital da ACM foi gerado para a realização do treinamento e de experimentos sobre a implementação. As principais contribuições desta pesquisa são o modelo de extração de metadados e classificação de documentos de forma integrada e a descrição dos documentos através de metadados armazenados em um ontologia, a OntoDoc. / The main purpose of the Semantic Web is to provide machine processable metadata that describes the semantics of resources to facilitate the search, filter, condense, or negotiate knowledge for their human users. In this context, digital libraries are applications where the semantic annotation process of information available in the Web is beginning. Digital library can be defined as a collection of digital resources selected by some criteria, with some organization and available through distributed network retrieval. To facilitate the retrieval process, metadata are applied to describe stored content. However, manual metadata generation is a complex task, time-consuming and error-prone. Thus, automatic or semiautomatic metadata generation would be great help to the authors, subtracting this task from the document publishing process. The research in this work approached this problem through the developing of a metadata extractor that populates a document ontology and classify the document according to a predefined hierarchy. The document ontology OntoDoc was created to store and to make available all the extracted metadata, as well as the obtained document classification. The implementation aimed on Computer Science papers and used the ACM Computing Classification system in the document classification task. A sample set extracted from the ACM Digital Libray was generated for implementation training and validation. The main contributions of this work are the integrated metadata extraction and classification model and the description of documents through a metadata stored in an ontology.

Automation of document handling in the construction industries could save large amounts of time, effort and money and classifying a document is an important step in that automation. In the field of machine learning, lots of research have been done on perfecting the algorithms and techniques, but there are many areas where those techniques could be used that has not yet been studied. In this study I looked at how effectively the machine learning algorithm multinomial Naïve-Bayes would be able to classify 1427 documents split up into 19 different categories from a construction project. The experiment achieved an accuracy of 92.7% and the paper discusses some of the ways that accuracy can be improved. However, data extraction proved to be a bottleneck and only 66% of the original documents could be used for testing the classifier.

Unlike single-label document classification, where each document exactly belongs to a single category, when the document is classified into two or more categories, known as multi-label file, how to classify such documents accurately has become a hot research topic in recent years. In this paper, we propose a algorithm named fuzzy similarity measure multi-label K nearest neighbors(FSMLKNN) which combines a fuzzy similarity measure with the multi-label K nearest neighbors(MLKNN) algorithm for multi-label document classification, the algorithm improved fuzzy similarity measure to calculate the similarity between a document and the center of cluster similarity, and proposed algorithm can significantly improve the performance and accuracy for multi-label document classification. In the experiment, we compare FSMLKNN and the existing classification methods, including decision tree C4.5, support vector machine(SVM) and MLKNN algorithm, the experimental results show that, FSMLKNN method is better than others.