Kernel Discriminant Analysis Using Triangular Kernel for Semantic Scene Classification

Semantic scene classification is a challenging research problem that aims to categorise images into semantic classes such as beaches, sunsets or mountains. This prob-lem can be formulated as multi-labeled classification prob-lem where an image can belong to more than one concep-tual class such as sunsets and beaches at the same time. Re-cently, Kernel Discriminant Analysis combined with spec-tral regression (SR-KDA) has been successfully used for face, text and spoken letter recognition. But SR-KDA method works only with positive definite symmetric matri-ces. In this paper, we have modified this method to support both definite and indefinite symmetric matrices. The main idea is to use LDLT decomposition instead of Cholesky decomposition. The modified SR-KDA is applied to scene database involving 6 concepts. We validate the advocated approach and demonstrate that it yields significant perfor-mance gains when conditionally positive definite triangular kernel is used instead of positive definite symmetric kernels such as linear, polynomial or RBF. The results also indicate performance gains when compared with the state-of-the art multi-label methods for semantic scene classification.

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

Classificação multi-etiqueta hierárquica de textos segundo a taxonomia ACM

Mestrado em Engenharia InformáticaMuitos dos trabalhos de classificação existentes na literatura, envolvem a atribuição a cada instância (exemplo) de uma única classe, de entre um conjunto pré-definido de classes normalmente pequeno e organizado de forma plana. Porém, existem problemas de classificação mais complexos, em que a cada instância é possível atribuir mais do que uma classe, podendo as classes, estar organizadas numa estrutura hierárquica. Para estes problemas, existe um conjunto de abordagens para lidar com o facto de uma instância poder pertencer a mais do que uma classe (classificação multi-etiqueta). Existem também abordagens para lidar com a organização hierárquica das classes (classificação hierárquica). Esta dissertação, apresenta um estudo das abordagens e conceitos de classificação multi-etiqueta e hierárquica, aplicados à classificação de documentos de texto. Trata-se, portanto, de um problema de classificação, em que as instâncias são documentos de texto, que podem pertencer a mais do que uma classe e estas encontram-se organizadas hierarquicamente. Nos problemas de classificação de texto, uma fase importante, é o pré-processamento dos documentos. Um processo transformativo, aplicado normalmente para reduzir o número de termos de um documento, de forma a obter uma representação dos documentos, mais adequada para as fases seguintes. Nesta dissertação, são também estudadas as várias tarefas de pré-processamento que podem ser realizadas, como por exemplo, remoção de stopwords, stemming, esquemas de atribuição de pesos aos termos. No estudo experimental realizado, foi utilizado o esquema de classificação ACM (Computing Classification System), que define um conjunto de classes, organizadas hierarquicamente, nas áreas científicas no campo da computação. O estudo experimental realizado, consistiu no desenvolvimento de uma solução para automatizar a navegação e recolha de documentos classificados da biblioteca digital ACM, pré-processamento dos documentos, construção e aplicação de diferentes classificadores a documentos ainda não classificados e por fim a avaliação do seu desempenho de previsão. Foi proposta uma metodologia para classificação multi-etiqueta hierárquica que combina as abordagens usadas na classificação multi-etiqueta e na classificação hierárquica que se mostrou adequada para a resolução destes problemas

Understanding patient experience from online medium

Improving patient experience at hospitals leads to better health outcomes. To improve this, we must first understand and interpret patients' written feedback. Patient-generated texts such as patient reviews found on RateMD, or online health forums found on WebMD are venues where patients post about their experiences. Due to the massive amounts of patient-generated texts that exist online, an automated approach to identifying the topics from patient experience taxonomy is the only realistic option to analyze these texts. However, not only is there a lack of annotated taxonomy on these media, but also word usage is colloquial, making it challenging to apply standardized NLP technique to identify the topics that are present in the patient-generated texts. Furthermore, patients may describe multiple topics in the patient-generated texts which drastically increases the complexity of the task. In this thesis, we address the challenges in comprehensively and automatically understanding the patient experience from patient-generated texts. We first built a set of rich semantic features to represent the corpus which helps capture meanings that may not typically be captured by the bag-of-words (BOW) model. Unlike the BOW model, semantic feature representation captures the context and in-depth meaning behind each word in the corpus. To the best of our knowledge, no existing work in understanding patient experience from patient-generated texts delves into which semantic features help capture the characteristics of the corpus. Furthermore, patients generally talk about multiple topics when they write in patient-generated texts, and these are frequently interdependent of each other. There are two types of topic interdependencies, those that are semantically similar, and those that are not. We built a constraint-based deep neural network classifier to capture the two types of topic interdependencies and empirically show the classification performance improvement over the baseline approaches. Past research has also indicated that patient experiences differ depending on patient segments [1-4]. The segments can be based on demographics, for instance, by race, gender, or geographical location. Similarly, the segments can be based on health status, for example, whether or not the patient is taking medication, whether or not the patient has a particular disease, or whether or not the patient is readmitted to the hospital. To better understand patient experiences, we built an automated approach to identify patient segments with a focus on whether the person has stopped taking the medication or not. The technique used to identify the patient segment is general enough that we envision the approach to be applicable to other types of patient segments. With a comprehensive understanding of patient experiences, we envision an application system where clinicians can directly read the most relevant patient-generated texts that pertain to their interest. The system can capture topics from patient experience taxonomy that is of interest to each clinician or designated expert, and we believe the system is one of many approaches that can ultimately help improve the patient experience