Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam

Dialogue-Oriented Review Summary Generation for Spoken Dialogue Recommendation Systems

In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13% compared to SVM models and an improvement of 36% over a heuristic rule baseline. Experiments also show that the decision-tree-based phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively

A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners

Feature Extraction and Duplicate Detection for Text Mining: A Survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

Methods for constructing an opinion network for politically controversial topics

The US presidential race, the re-election of President Hugo Chavez, and the economic crisis in Greece and other European countries are some of the controversial topics being played on the news everyday. To understand the landscape of opinions on political controversies, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on specific aspects of these topics. The work described in this thesis aims to automatically derive a map of the opinions-people network from news and other Web docu- ments. The focus is on acquiring opinions held by various stakeholders on politi- cally controversial topics. This opinions-people network serves as a knowledge- base of opinions in the form of (opinion holder) (opinion) (topic) triples. Our system to build this knowledge-base makes use of online news sources in order to extract opinions from text snippets. These sources come with a set of unique challenges. For example, processing text snippets involves not just iden- tifying the topic and the opinion, but also attributing that opinion to a specific opinion holder. This requires making use of deep parsing and analyzing the parse tree. Moreover, in order to ensure uniformity, both the topic as well the opinion holder should be mapped to canonical strings, and the topics should also be organized into a hierarchy. Our system relies on two main components: i) acquiring opinions which uses a combination of techniques to extract opinions from online news sources, and ii) organizing topics which crawls and extracts de- bates from online sources, and organizes these debates in a hierarchy of political controversial topics. We present systematic evaluations of the different compo- nents of our system, and show their high accuracies. We also present some of the different kinds of applications that require political analysis. We present some application requires political analysis such as identifying flip-floppers, political bias, and dissenters. Such applications can make use of the knowledge-base of opinions.Kontroverse Themen wie das US-Präsidentschaftsrennen, die Wiederwahl von Präsident Hugo Chavez, die Wirtschaftskrise in Griechenland sowie in anderen europäischen Ländern werden täglich in den Nachrichten diskutiert. Um die Bandbreite verschiedener Meinungen zu politischen Kontroversen zu verstehen, ist es hilfreich herauszufinden, welcher Politiker bzw. Interessenvertreter welchen Standpunkt (Pro oder Contra) bezüglich spezifischer Aspekte dieser Themen einnimmt. Diese Dissertation beschreibt ein Verfahren, welches automatisch eine Übersicht des Meinung-Mensch-Netzwerks aus aktuellen Nachrichten und anderen Web-Dokumenten ableitet. Der Fokus liegt hierbei auf dem Erfassen von Meinungen verschiedener Interessenvertreter bezüglich politisch kontroverser Themen. Dieses Meinung-Mensch-Netzwerk dient als Wissensbasis von Meinungen in Form von Tripeln: (Meinungsvertreter) (Meinung) (Thema). Um diese Wissensbasis aufzubauen, nutzt unser System Online-Nachrichten und extrahiert Meinungen aus Textausschnitten. Quellen von Online-Nachrichten stellen eine Reihe von besonderen Anforderungen an unser System. Zum Beispiel umfasst die Verarbeitung von Textausschnitten nicht nur die Identifikation des Themas und der geschilderten Meinung, sondern auch die Zuordnung der Stellungnahme zu einem spezifischen Meinungsvertreter.Dies erfordert eine tiefgründige Analyse sowie eine genaue Untersuchung des Syntaxbaumes. Um die Einheitlichkeit zu gewährleisten, müssen darüber hinaus Thema sowie Meinungsvertreter auf ein kanonisches Format abgebildet und die Themen hierarchisch angeordnet werden. Unser System beruht im Wesentlichen auf zwei Komponenten: i) Erkennen von Meinungen, welches verschiedene Techniken zur Extraktion von Meinungen aus Online-Nachrichten beinhaltet, und ii) Erkennen von Beziehungen zwischen Themen, welches das Crawling und Extrahieren von Debatten aus Online-Quellen sowie das Organisieren dieser Debatten in einer Hierarchie von politisch kontroversen Themen umfasst. Wir präsentieren eine systematische Evaluierung der verschiedenen Systemkomponenten, welche die hohe Genauigkeit der von uns entwickelten Techniken zeigt. Wir diskutieren außerdem verschiedene Arten von Anwendungen, die eine politische Analyse erfordern, wie zum Beispiel die Erkennung von Opportunisten, politische Voreingenommenheit und Dissidenten. All diese Anwendungen können durch die Wissensbasis von Meinungen umfangreich profitieren