Similarity and Diversity in Information Retrieval

Inter-document similarity is used for clustering, classification, and other purposes within information retrieval. In this thesis, we investigate several aspects of document similarity. In particular, we investigate the quality of several measures of inter-document similarity, providing a framework suitable for measuring and comparing the effectiveness of inter-document similarity measures. We also explore areas of research related to novelty and diversity in information retrieval. The goal of diversity and novelty is to be able to satisfy as many users as possible while simultaneously minimizing or eliminating duplicate and redundant information from search results. In order to evaluate the effectiveness of diversity-aware retrieval functions, user query logs and other information captured from user interactions with commercial search engines are mined and analyzed in order to uncover various informational aspects underlying queries, which are known as subtopics. We investigate the suitability of implicit associations between document content as an alternative to subtopic mining. We also explore subtopic mining from document anchor text and anchor links. In addition, we investigate the suitability of inter-document similarity as a measure for diversity-aware retrieval models, with the aim of using measured inter-document similarity as a replacement for diversity-aware evaluation models that rely on subtopic mining. Finally, we investigate the suitability and application of document similarity for requirements traceability. We present a fast algorithm that uncovers associations between various versions of frequently edited documents, even in the face of substantial changes

Descriptive document clustering via discriminant learning in a co-embedded space of multilevel similarities

Descriptive document clustering aims at discovering clusters of semantically interrelated documents together with meaningful labels to summarize the content of each document cluster. In this work, we propose a novel descriptive clustering framework, referred to as CEDL. It relies on the formulation and generation of 2 types of heterogeneous objects, which correspond to documents and candidate phrases, using multilevel similarity information. CEDL is composed of 5 main processing stages. First, it simultaneously maps the documents and candidate phrases into a common co‐embedded space that preserves higher‐order, neighbor‐based proximities between the combined sets of documents and phrases. Then, it discovers an approximate cluster structure of documents in the common space. The third stage extracts promising topic phrases by constructing a discriminant model where documents along with their cluster memberships are used as training instances. Subsequently, the final cluster labels are selected from the topic phrases using a ranking scheme using multiple scores based on the extracted co‐embedding information and the discriminant output. The final stage polishes the initial clusters to reduce noise and accommodate the multitopic nature of documents. The effectiveness and competitiveness of CEDL is demonstrated qualitatively and quantitatively with experiments using document databases from different application fields

Navegador ontológico matemático-NOMAT

The query algorithms in search engines use indexing, contextual analysis and ontologies, among other techniques, for text search. However, they do not use equations due to their writing complexity. NOMAT is a prototype of mathematical expression search engine that seeks information both in thesaurus and internet, using ontological tool for filtering and contextualizing information and LaTeX editor for the symbols in these expressions. This search engine was created to support mathematical research. Compared to other Internet search engines, NOMAT does not require prior knowledge of LaTeX, because has an editing tool which enables writing directly the symbols that make up the mathematical expression of interest. The results obtained were accurate and contextualized, compared to other commercial and no-commercial search engines.Los algoritmos de consulta de los motores de búsqueda utilizan indexación, análisis contextual y ontologías, entre otras técnicas, para la búsqueda de texto. Sin embargo, no utilizan ecuaciones debido a su complejidad de escritura. Nomat es un prototipo de motor de búsqueda de expresión matemática que busca información tanto en tesauro como en Internet, utilizando la Herramienta ontológica para filtrar y contextualizar información y editor de látex para los símbolos de estas expresiones. Este buscador fue creado para apoyar la investigación matemática. En comparación con otros motores de búsqueda de Internet, Nomat no requiere conocimientos previos de látex, ya que cuenta con una herramienta de edición que permite escribir directamente los símbolos que componen la expresión matemática de interés. Los resultados obtenidos fueron precisos y contextualizados, en comparación con otros motores de búsqueda comerciales y no comerciales

Improving Retrieval Accuracy in Main Content Extraction from HTML Web Documents

The rapid growth of text based information on the World Wide Web and various applications making use of this data motivates the need for efficient and effective methods to identify and separate the “main content” from the additional content items, such as navigation menus, advertisements, design elements or legal disclaimers. Firstly, in this thesis, we study, develop, and evaluate R2L, DANA, DANAg, and AdDANAg, a family of novel algorithms for extracting the main content of web documents. The main concept behind R2L, which also provided the initial idea and motivation for the other three algorithms, is to use well particularities of Right-to-Left languages for obtaining the main content of web pages. As the English character set and the Right-to-Left character set are encoded in different intervals of the Unicode character set, we can efficiently distinguish the Right-to-Left characters from the English ones in an HTML file. This enables the R2L approach to recognize areas of the HTML file with a high density of Right-to-Left characters and a low density of characters from the English character set. Having recognized these areas, R2L can successfully separate only the Right-to-Left characters. The first extension of the R2L, DANA, improves effectiveness of the baseline algorithm by employing an HTML parser in a post processing phase of R2L for extracting the main content from areas with a high density of Right-to-Left characters. DANAg is the second extension of the R2L and generalizes the idea of R2L to render it language independent. AdDANAg, the third extension of R2L, integrates a new preprocessing step to normalize the hyperlink tags. The presented approaches are analyzed under the aspects of efficiency and effectiveness. We compare them to several established main content extraction algorithms and show that we extend the state-of-the-art in terms of both, efficiency and effectiveness. Secondly, automatically extracting the headline of web articles has many applications. We develop and evaluate a content-based and language-independent approach, TitleFinder, for unsupervised extraction of the headline of web articles. The proposed method achieves high performance in terms of effectiveness and efficiency and outperforms approaches operating on structural and visual features.Das rasante Wachstum von textbasierten Informationen im World Wide Web und die Vielfalt der Anwendungen, die diese Daten nutzen, macht es notwendig, effiziente und effektive Methoden zu entwickeln, die den Hauptinhalt identifizieren und von den zusätzlichen Inhaltsobjekten wie z.B. Navigations-Menüs, Anzeigen, Design-Elementen oder Haftungsausschlüssen trennen. Zunächst untersuchen, entwickeln und evaluieren wir in dieser Arbeit R2L, DANA, DANAg und AdDANAg, eine Familie von neuartigen Algorithmen zum Extrahieren des Inhalts von Web-Dokumenten. Das grundlegende Konzept hinter R2L, das auch zur Entwicklung der drei weiteren Algorithmen führte, nutzt die Besonderheiten der Rechts-nach-links-Sprachen aus, um den Hauptinhalt von Webseiten zu extrahieren. Da der lateinische Zeichensatz und die Rechts-nach-links-Zeichensätze durch verschiedene Abschnitte des Unicode-Zeichensatzes kodiert werden, lassen sich die Rechts-nach-links-Zeichen leicht von den lateinischen Zeichen in einer HTML-Datei unterscheiden. Das erlaubt dem R2L-Ansatz, Bereiche mit einer hohen Dichte von Rechts-nach-links-Zeichen und wenigen lateinischen Zeichen aus einer HTML-Datei zu erkennen. Aus diesen Bereichen kann dann R2L die Rechts-nach-links-Zeichen extrahieren. Die erste Erweiterung, DANA, verbessert die Wirksamkeit des Baseline-Algorithmus durch die Verwendung eines HTML-Parsers in der Nachbearbeitungsphase des R2L-Algorithmus, um den Inhalt aus Bereichen mit einer hohen Dichte von Rechts-nach-links-Zeichen zu extrahieren. DANAg erweitert den Ansatz des R2L-Algorithmus, so dass eine Sprachunabhängigkeit erreicht wird. Die dritte Erweiterung, AdDANAg, integriert eine neue Vorverarbeitungsschritte, um u.a. die Weblinks zu normalisieren. Die vorgestellten Ansätze werden in Bezug auf Effizienz und Effektivität analysiert. Im Vergleich mit mehreren etablierten Hauptinhalt-Extraktions-Algorithmen zeigen wir, dass sie in diesen Punkten überlegen sind. Darüber hinaus findet die Extraktion der Überschriften aus Web-Artikeln vielfältige Anwendungen. Hierzu entwickeln wir mit TitleFinder einen sich nur auf den Textinhalt beziehenden und sprachabhängigen Ansatz. Das vorgestellte Verfahren ist in Bezug auf Effektivität und Effizienz besser als bekannte Ansätze, die auf strukturellen und visuellen Eigenschaften der HTML-Datei beruhen

FROM USER-GENERATED-CONTENT TO STRUCTURED KNOWLEDGE EXPLORING MULTI-ASPECT SENTENCE REPRESENTATION AND PROTOTYPE HIERARCHY BASED CATEGORIZATION FOR ORGANIZATION OF TEXT COLLECTIONS

Ph.DDOCTOR OF PHILOSOPH

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Agile Business Suite Log Analysis and Stochastic Modeling of Transactions

Agile Business Suite (ABSuite) is an application development and deployment product that can define, generate and manage complete, highly scalable, real world, platform independent applications. The product was developed by Unisys Corporation with the intention to allow developers to rapidly develop their complete applications without having to think about low level platform dependent implementation details thus saving them from having to write thousands of lines of code. Many enterprises like Banks, Healthcare Facilities, Traffic Management, Financial Institutions and etc. use ABSuite to develop their applications and automate their business logic. Once deployed on the application servers, the generated applications are managed by the ABSuite Runtime framework. One important function of the ABSuite Runtime is to maintain a set of log files that contain immense information on system behavior, communication between the system and the runtime framework and user interaction with the system. There are various kinds of log files that are generated by the ABSuite Runtime, each having its own format and vast amounts of hidden knowledge stored in them. These log files contain important information regarding usage patterns, system failure patterns and other performance bottlenecks. Thus proper analysis of these log files is necessary to obtain vital information which will help in optimizing system performance, easing maintenance tasks, identifying hidden bugs and abnormal behavior and adopting better design strategies. Log files are produced by almost all devices, systems and protocols. In general, the analysis of any kind of log file is not an easy task. There are several challenges that need to be addressed first in order to obtain proper and accurate results. The first issue is directly related to the fact that ABSuite log files are often huge and heterogeneous in nature. Standard Algorithms fail in such conditions and thus the task of log analysis requires a different approach. Secondly, identification of required information and the log files storing such information requires domain specific knowledge and expertise regarding the generated log files. Without prior information on what to look for and where to look for, the analysis process will become impossible. Last but not least is the issue of unstandardized format of each of the ABSuite log files which makes processing a difficult task. Keeping the above things in mind, this thesis presents some of the ideas behind developing a tool that automatically analyzes the generated ABSuite log files and extracts information that will help the developers to optimize the application’s performance and reduce the system’s downtime and maintenance cost. vii In this thesis we discuss three stages of analysis. The first stage (Ispec Analysis) finds basic statistical parameters like trigger count, frequency and probability distribution of each transaction occurring in the system. This information is used in subsequent stages to obtain more accurate results and hypothesis. In addition, We also present a way to use the results of this stage to estimate usage and traffic patterns, peak loading conditions and heavily used modules of the system. The second stage (Response Time Analysis) obtains mean time to response for each transaction. This information is used to identify transactions with high latencies and find the most likely cause of those latencies. The third stage (Exception Analysis) finds which transactions frequently generate exceptions and how many types of exceptions are generated by each transaction. The results of this stage help in identifying the number, type, severity and cause of exceptions generated by the system. The results of this stage can also help in taking high level decisions like whether system resources need to be increased to avoid deadlocks, whether there is any need to increase bandwidth allocation, whether there is any need to change the current design of the system and etc. Finally, we present and compare the analysis results of all the three stages for two sample applications (developed using ABSuite) with known usage characteristics. We also explain how the developers can use the results of each stage of analysis to better optimize their systems using the two sample applications as case study

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one