An Incrementally Trainable Statistical Approach to Information Extraction Based on Token Classification and Rich Context Models

Size: px
Start display at page:

Download "An Incrementally Trainable Statistical Approach to Information Extraction Based on Token Classification and Rich Context Models"

Transcription

1 Dissertation (Ph.D. Thesis) An Incrementally Trainable Statistical Approach to Information Extraction Based on Token Classification and Rich Context Models Christian Siefkes Disputationen: 16th February 2007 Primary Supervisor: Prof. Dr. Heinz F. Schweppe Database and Information Systems Group Fachbereich Mathematik und Informatik Freie Universität Berlin

2 Supervisors: Prof. Dr. Heinz F. Schweppe Database and Information Systems Group Institute for Computer Science Freie Universität Berlin Prof. Dr. Bernhard Thalheim Systems for Information Management Institute of Computer Science and Applied Mathematics Christian-Albrechts-Universität zu Kiel This thesis has been supported by the German Research Society, Berlin-Brandenburg Graduate School in Distributed Information Systems (DFG grant no. GRK 316).

3 Dedicated to the memory of my parents, Uta Siefkes ( ) and Harm Siefkes ( )

4

5 Abstract Most of the information stored in digital form is hidden in natural language (NL) texts. While information retrieval (IR) helps to locate documents which might contain the facts needed, there is no way to answer queries. The purpose of information extraction (IE) is to find desired pieces of information in NL texts and store them in a form that is suitable for automatic querying and processing. The goal of this thesis has been the development and evaluation of a trainable statistical IE approach. This approach introduces new functionality not supported by current IE systems, such as support for incremental training to reduce the human training effort by allowing a more interactive workflow. The IE system introduced in this thesis is designed as a generic framework for statistical classification-based information extraction that allows modifying and exchanging all core components (such as classification algorithm, context representations, tagging strategies) independently of each other. The thesis includes a systematic analysis of switching one such component (the tagging strategies). Several new sources of information are explored for improving extraction quality. Especially we introduce rich tree-based context representations that combine document structure and generic XML markup with more conventional linguistic and semantic sources of information. Preparing these rich context representations makes it necessary to unify various and partially conflicting sources of information (such as structural markup and linguistic annotations) in XML-style trees. For this purpose, we develop a merging algorithm that can repair nesting errors and related problems in XML-like input. As the core of the classification-based IE approach, we introduce a generic classification algorithm (Winnow+OSB) that combines online learning with novel feature combination techniques. We show that this algorithm is not only suitable for information extraction, but also for other tasks such as text classification. Among other good results, the classifier was found to be one of the two best filters submitted for the 2005 Spam Filtering Task of the Text REtrieval Conference (TREC). The thesis includes a detailed evaluation of the resulting IE which shows that the results reached by our system are better than or competitive with those of other stateof-the-art IE systems. The evaluation includes an ablation study that measures the influence of various factors on the overall results and finds that all of them contribute to the good results of our system. It also includes an analysis of the utility of interactive incremental training that confirms that this newly introduced training regimen can be very helpful for reducing the human training effort. The quantitative evaluation is complemented with an analysis of the kinds of mistakes made during extraction and their likely causes that allows a better understanding of where and how we can expect further improvements in information extraction quality to be made and which limits might exist for information extraction systems in general.

6

7 Wir sehen ein kompliziertes Netz von Ähnlichkeiten, die einander übergreifen und kreuzen. Ähnlichkeiten im Großen und Kleinen. Ich kann diese Ähnlichkeiten nicht besser charakterisieren als durch das Wort Familienähnlichkeiten ; denn so übergreifen und kreuzen sich die verschiedenen Ähnlichkeiten, die zwischen den Gliedern einer Familie bestehen: Wuchs, Gesichtszüge, Augenfarbe, Gang, Temperament, etc. etc. Und ich werde sagen: die Spiele bilden eine Familie. [... ] Wie würden wir denn jemandem erklären, was ein Spiel ist? Ich glaube, wir werden ihm Spiele beschreiben, und wir könnten der Beschreibung hinzufügen: das, und Ähnliches, nennt man,spiele. Und wissen wir selbst denn mehr? Können wir etwa nur dem Anderen nicht genau sagen, was ein Spiel ist? Aber das ist nicht Unwissenheit. Wir kennen die Grenzen nicht, weil keine gezogen sind. Ludwig Wittgenstein, Philosophische Untersuchungen Leeloo: Hello. Korben Dallas: Oh, so you speak English now. Leeloo: Yes. I learned. The Fifth Element (1997)

8

9 Contents 1 Introduction Motivation and Goals Contributions Outline of this Work Acknowledgments I The Field of Information Extraction 13 2 Information Extraction Information Retrieval, Text Mining, and Other Related Areas Overview and Classification of Approaches Architecture and Workflow Tasks to Handle Architecture of a Typical IE System Active Learning and Incremental Learning Workflow Statistical Approaches Probabilistic Semantic Parsing Hidden Markov Models Maximum Entropy Markov Models and Conditional Random Fields Token Classification Fragment Classification and Bayesian Networks Non-Statistical Approaches Covering Algorithms Relational Rule Learners Wrapper Induction Hybrid Approaches Knowledge-based Approaches Comparison of Existing Approaches Types of Tasks Handled Types of Texts Handled Considered Features Tagging Requirements and Learning Characteristics

10 Contents II Analysis 47 7 Aims and Requirements Aims of Our Approach Further Requirements Chosen Approach Non-Goals Assumptions Novel Assumptions General Assumptions Suitability of Tasks Target Schemas and Input/Output Models Target Schemas Formats for Input Texts Input Formats for Answer Keys Serialization of Extracted Attribute Values III Algorithms and Models Modeling Information Extraction as a Classification Task Idea and Concept Tagging Strategies Classification Algorithm and Feature Combination Techniques The Winnow Classification Algorithm Feature Combination Techniques Alternative Classification Algorithms and Implementations Preprocessing and Context Representation Preprocessing Tree-based Context Representation Tokenization Merging Conflicting and Incomplete XML Markup Introduction and Motivation Types of Errors in XML-like Input Configurable Settings and Heuristics for Repair Algorithm Description Limitations Application in Our Approach Related Work Weakly Hierarchical Extraction 99 2

11 Contents 14.1 Introduction Inheritance Hierarchies of Attributes Strictly Hierarchical Approach and Related Problems Weakly Hierarchical Approach Integration into Information Extraction Approach IV Evaluation Evaluation Goals and Metrics Goals and Limitations of Quantitative Evaluation Evaluation Methodology Evaluation Metrics Text Classification Experiments Introduction Text Classification Setup for Spam Filtering Experimental Results on the SpamAssassin Corpus TREC Spam Filtering Challenge Concluding Remarks Extraction of Attribute Values Test Corpora Evaluation Results for the Seminar Announcements Corpus Evaluation Results for the Corporate Acquisitions Corpus Ablation Study and Utility of Incremental Training Ablation Study Utility of Interactive Incremental Training Comparison of Tagging Strategies Idea and Setup Comparison Results Analysis Weakly Hierarchical Extraction Experimental Setup Experimental Results Concluding Remarks Mistake Analysis Mistake Types Distribution of Mistakes Type Confusion Additional Manual Analysis Length Analysis

12 Contents V Conclusions Conclusion and Outlook Discussion of Results Summary of Contributions Future Work Bibliography 175 A Schema for Augmented Text 185 B Curriculum Vitae 189 C Zusammenfassung in deutscher Sprache 191 4

13 List of Tables 2.1 Applications of IR, IE, and Text Mining Overview of the Selected Approaches and Systems Example of External Answer Keys Properties of Tagging Strategies Labeling Example SBPH 5 Feature Combinations Containing f Features Generated by SBPH and OSB Regular Expressions Used for Tokenization Promotion and Demotion Factors Threshold Thickness Comparison of SBPH and OSB with Different Feature Storage Sizes Utility of Single Tokens (Unigrams) Sliding Window Size Preprocessing Comparison with Naive Bayes and CRM Results on the Seminar Corpus System Comparison on the Seminar Corpus (F-measure) Results on the Acquisitions Corpus System Comparison on the Acquisitions Corpus (F-measure) Ablation Study: Seminar Announcements Ablation Study: Corporate Acquisitions Results with Incremental Feedback Incremental Feedback: User Effort for Correcting the Training Set Incremental Feedback: User Effort for Correcting the Evaluation Set F-measure Percentages for Incremental Training F-measure Percentages for Batch Training Incremental Training: Significance of Changes Compared to IOB Batch Training: Significance of Changes Compared to IOB Recall Reached by Supertype Recognizers on Subtype Answer Keys Seminar Corpus: Length Distribution of Answer Keys Acquisitions Corpus: Length Distribution of Answer Keys

14

15 List of Figures 3.1 Tasks to Be Handled Architecture of a Typical IE System Sample Interface: Information Extraction from Messages Sample Input Text Sample Text with Inline Annotations Partial DOM Tree of a Simple HTML Document with Linguistic Annotations Processed File from the Seminar Announcements Corpus Inverted Subtree of the Elements Considered for a Context Representation Learning Curve for the best setting (Winnow 1.23,0.83,5%, 600,000 features, OSB 5 ) ROC curve for the best filters (Source: [Cor05, Fig. 2]) Results on the Seminar Corpus Seminar Corpus: Precision and Recall Improvements System Comparison: F-measure Averages on the Seminar Corpus Results on the Acquisitions Corpus Acquisitions Corpus: Precision and Recall Improvements System Comparison: F-measure Averages on the Acquisitions Corpus Ablation Study: Seminar Announcements Ablation Study: Corporate Acquisitions Incremental Feedback: Learning Curve (average precision, recall, and F-measure on all documents processed so far) Incremental Feedback: Correct, Missing, and Spurious Predictions in the Training Set Incremental Feedback: Correct, Missing, and Spurious Predictions in the Evaluation Set Seminar Corpus: Inheritance Hierarchy Acquisitions Corpus: Inheritance Hierarchy Seminar Corpus: F-measure Results Acquisitions Corpus: F-measure Results Acquisitions Corpus: Collapsing Short and Long Names Seminar Corpus: Temporal Predictions Seminar Corpus: Mistakes Combinations

16 List of Figures 21.2 Seminar Corpus: Distribution of Mistake Types Acquisitions Corpus: Mistakes Combinations Acquisitions Corpus: Distribution of Mistake Types Seminar Corpus: Confusion Matrix (expected type predicted type) Acquisitions Corpus: Confusion Matrix (expected type predicted type) Seminar Corpus: Precision and Recall by Token Length Seminar Corpus: F-Measure by Token Length Seminar Corpus: Weighted Averages by Token Length Acquisitions Corpus: Precision and Recall by Token Length Acquisitions Corpus: F-Measure by Token Length Acquisitions Corpus: Weighted Averages by Token Length

Search Engines Chapter 2 Architecture. 14.4.2011 Felix Naumann

Search Engines Chapter 2 Architecture. 14.4.2011 Felix Naumann Search Engines Chapter 2 Architecture 14.4.2011 Felix Naumann Overview 2 Basic Building Blocks Indexing Text Acquisition Text Transformation Index Creation Querying User Interaction Ranking Evaluation

More information

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. ~ Spring~r

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. ~ Spring~r Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures ~ Spring~r Table of Contents 1. Introduction.. 1 1.1. What is the World Wide Web? 1 1.2. ABrief History of the Web

More information

Search and Information Retrieval

Search and Information Retrieval Search and Information Retrieval Search on the Web 1 is a daily activity for many people throughout the world Search and communication are most popular uses of the computer Applications involving search

More information

Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering

Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering Christian Siefkes 1, Fidelis Assis 2, Shalendra Chhabra 3, and William S. Yerazunis 4 1 Berlin-Brandenburg Graduate School

More information

Machine Learning for natural language processing

Machine Learning for natural language processing Machine Learning for natural language processing Introduction Laura Kallmeyer Heinrich-Heine-Universität Düsseldorf Summer 2016 1 / 13 Introduction Goal of machine learning: Automatically learn how to

More information

Projektgruppe. Information Extraction An Incomplete Overview

Projektgruppe. Information Extraction An Incomplete Overview Projektgruppe Henning Wachsmuth Information Extraction An Incomplete Overview 12. Mai 2010 1 Einführungsvorträge Verfassen von Seminarvortrag und paper Prof. Dr. Gregor Engels, Donnerstag 15.4., 16h-18h

More information

GETTING FEEDBACK REALLY FAST WITH DESIGN THINKING AND AGILE SOFTWARE ENGINEERING

GETTING FEEDBACK REALLY FAST WITH DESIGN THINKING AND AGILE SOFTWARE ENGINEERING GETTING FEEDBACK REALLY FAST WITH DESIGN THINKING AND AGILE SOFTWARE ENGINEERING Dr. Tobias Hildenbrand & Christian Suessenbach, SAP AG Entwicklertag Karlsruhe, 22 May 2014 Ich wollte Mitarbeiter so motivieren,

More information

Search Result Optimization using Annotators

Search Result Optimization using Annotators Search Result Optimization using Annotators Vishal A. Kamble 1, Amit B. Chougule 2 1 Department of Computer Science and Engineering, D Y Patil College of engineering, Kolhapur, Maharashtra, India 2 Professor,

More information

Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering

Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering Christian Siefkes 1, Fidelis Assis 2, Shalendra Chhabra 3, and William S. Yerazunis 4 1 Berlin-Brandenburg Graduate School

More information

Experiments in Web Page Classification for Semantic Web

Experiments in Web Page Classification for Semantic Web Experiments in Web Page Classification for Semantic Web Asad Satti, Nick Cercone, Vlado Kešelj Faculty of Computer Science, Dalhousie University E-mail: {rashid,nick,vlado}@cs.dal.ca Abstract We address

More information

Stefan Engelberg (IDS Mannheim), Workshop Corpora in Lexical Research, Bucharest, Nov. 2008 [Folie 1]

Stefan Engelberg (IDS Mannheim), Workshop Corpora in Lexical Research, Bucharest, Nov. 2008 [Folie 1] Content 1. Empirical linguistics 2. Text corpora and corpus linguistics 3. Concordances 4. Application I: The German progressive 5. Part-of-speech tagging 6. Fequency analysis 7. Application II: Compounds

More information

Exemplar for Internal Achievement Standard. German Level 1

Exemplar for Internal Achievement Standard. German Level 1 Exemplar for Internal Achievement Standard German Level 1 This exemplar supports assessment against: Achievement Standard 90885 Interact using spoken German to communicate personal information, ideas and

More information

Julia Englert, PhD Student. Curriculum Vitae

Julia Englert, PhD Student. Curriculum Vitae Julia Englert, PhD Student Curriculum Vitae Name: Nationality: Julia Valerie Englert German Date of Birth: April 14 th 1987 E-Mail: j.englert@uni-saarland.de Phone 0049-681-302-68563 Office Address: Saarland

More information

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING

Practical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction

More information

Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

More information

Natural Language to Relational Query by Using Parsing Compiler

Natural Language to Relational Query by Using Parsing Compiler Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 3, March 2015,

More information

Brauchen die Digital Humanities eine eigene Methodologie?

Brauchen die Digital Humanities eine eigene Methodologie? Deutsche DH, Passau 26.03.2014 Brauchen die Digital Humanities eine eigene Methodologie? 26. März 2014 Heyer / Niekler / Wiedemann 1 Übersicht Aspekte der Operationalisierung geistes- und sozialwissenschaftlicher

More information

Email Spam Detection Using Customized SimHash Function

Email Spam Detection Using Customized SimHash Function International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 1, Issue 8, December 2014, PP 35-40 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org Email

More information

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup

Detection. Perspective. Network Anomaly. Bhattacharyya. Jugal. A Machine Learning »C) Dhruba Kumar. Kumar KaKta. CRC Press J Taylor & Francis Croup Network Anomaly Detection A Machine Learning Perspective Dhruba Kumar Bhattacharyya Jugal Kumar KaKta»C) CRC Press J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor

More information

Stefan Engelberg (IDS Mannheim), Workshop Corpora in Lexical Research, Bucharest, Nov. 2008 [Folie 1]

Stefan Engelberg (IDS Mannheim), Workshop Corpora in Lexical Research, Bucharest, Nov. 2008 [Folie 1] Content 1. Empirical linguistics 2. Text corpora and corpus linguistics 3. Concordances 4. Application I: The German progressive 5. Part-of-speech tagging 6. Fequency analysis 7. Application II: Compounds

More information

Usability in SW-Engineering-Prozessen und in CMMI

Usability in SW-Engineering-Prozessen und in CMMI Workshop USABILITY VDE Prüf- und Zertifizierungsinstitut Strategiekreis i-12 Usability in SW-Engineering-Prozessen und in CMMI Helmut Thoma Schweizer Informatik Gesellschaft Lehrbeauftragter Universität

More information

A STUDY IN USER-CENTRIC DATA INTEGRATION

A STUDY IN USER-CENTRIC DATA INTEGRATION A STUDY IN USER-CENTRIC DATA INTEGRATION Heiner Stuckenschmidt 1 2, Jan Noessner 1, and Faraz Fallahi 3 1 School of Business Informatics and Mathematics, University of Mannheim. 68159 Mannheim. Germany

More information

Highly Scalable Discriminative Spam Filtering

Highly Scalable Discriminative Spam Filtering Highly Scalable Discriminative Spam Filtering Michael Brückner, Peter Haider, and Tobias Scheffer Max Planck Institute for Computer Science, Saarbrücken, Germany Humboldt Universität zu Berlin, Germany

More information

Bayesian Spam Filtering

Bayesian Spam Filtering Bayesian Spam Filtering Ahmed Obied Department of Computer Science University of Calgary amaobied@ucalgary.ca http://www.cpsc.ucalgary.ca/~amaobied Abstract. With the enormous amount of spam messages propagating

More information

Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg

Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg March 1, 2007 The catalogue is organized into sections of (1) obligatory modules ( Basismodule ) that

More information

Web Data Extraction: 1 o Semestre 2007/2008

Web Data Extraction: 1 o Semestre 2007/2008 Web Data : Given Slides baseados nos slides oficiais do livro Web Data Mining c Bing Liu, Springer, December, 2006. Departamento de Engenharia Informática Instituto Superior Técnico 1 o Semestre 2007/2008

More information

Overview. What is Information Retrieval? Classic IR: Some basics Link analysis & Crawlers Semantic Web Structured Information Extraction/Wrapping

Overview. What is Information Retrieval? Classic IR: Some basics Link analysis & Crawlers Semantic Web Structured Information Extraction/Wrapping Overview What is Information Retrieval? Classic IR: Some basics Link analysis & Crawlers Semantic Web Structured Information Extraction/Wrapping Hidir Aras, Digitale Medien 1 Agenda (agreed so far) 08.4:

More information

Network Big Data: Facing and Tackling the Complexities Xiaolong Jin

Network Big Data: Facing and Tackling the Complexities Xiaolong Jin Network Big Data: Facing and Tackling the Complexities Xiaolong Jin CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences (CAS) 2015-08-10

More information

An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them

An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them Vangelis Karkaletsis and Constantine D. Spyropoulos NCSR Demokritos, Institute of Informatics & Telecommunications,

More information

Mit einem Auge auf den mathema/schen Horizont: Was der Lehrer braucht für die Zukun= seiner Schüler

Mit einem Auge auf den mathema/schen Horizont: Was der Lehrer braucht für die Zukun= seiner Schüler Mit einem Auge auf den mathema/schen Horizont: Was der Lehrer braucht für die Zukun= seiner Schüler Deborah Löwenberg Ball und Hyman Bass University of Michigan U.S.A. 43. Jahrestagung für DidakEk der

More information

On Attacking Statistical Spam Filters

On Attacking Statistical Spam Filters On Attacking Statistical Spam Filters Gregory L. Wittel and S. Felix Wu Department of Computer Science University of California, Davis One Shields Avenue, Davis, CA 95616 USA Paper review by Deepak Chinavle

More information

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours. (International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

More information

Chapter 6. The stacking ensemble approach

Chapter 6. The stacking ensemble approach 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

More information

Data Mining & Data Stream Mining Open Source Tools

Data Mining & Data Stream Mining Open Source Tools Data Mining & Data Stream Mining Open Source Tools Darshana Parikh, Priyanka Tirkha Student M.Tech, Dept. of CSE, Sri Balaji College Of Engg. & Tech, Jaipur, Rajasthan, India Assistant Professor, Dept.

More information

Simple Language Models for Spam Detection

Simple Language Models for Spam Detection Simple Language Models for Spam Detection Egidio Terra Faculty of Informatics PUC/RS - Brazil Abstract For this year s Spam track we used classifiers based on language models. These models are used to

More information

TS3: an Improved Version of the Bilingual Concordancer TransSearch

TS3: an Improved Version of the Bilingual Concordancer TransSearch TS3: an Improved Version of the Bilingual Concordancer TransSearch Stéphane HUET, Julien BOURDAILLET and Philippe LANGLAIS EAMT 2009 - Barcelona June 14, 2009 Computer assisted translation Preferred by

More information

Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets Disambiguating Implicit Temporal Queries by Clustering Top Ricardo Campos 1, 4, 6, Alípio Jorge 3, 4, Gaël Dias 2, 6, Célia Nunes 5, 6 1 Tomar Polytechnic Institute, Tomar, Portugal 2 HULTEC/GREYC, University

More information

Verteilte Systeme 3. Dienstevermittlung

Verteilte Systeme 3. Dienstevermittlung VS32 Slide 1 Verteilte Systeme 3. Dienstevermittlung 3.2 Prinzipien einer serviceorientierten Architektur (SOA) Sebastian Iwanowski FH Wedel VS32 Slide 2 Prinzipien einer SOA 1. Definitionen und Merkmale

More information

SOFTWARE ENGINEERING PROGRAM

SOFTWARE ENGINEERING PROGRAM SOFTWARE ENGINEERING PROGRAM PROGRAM TITLE DEGREE TITLE Master of Science Program in Software Engineering Master of Science (Software Engineering) M.Sc. (Software Engineering) PROGRAM STRUCTURE Total program

More information

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group

Mining. Practical. Data. Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press. Taylor & Francis Group Practical Data Mining Monte F. Hancock, Jr. Chief Scientist, Celestech, Inc. CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor Ei Francis Group, an Informs

More information

8. Machine Learning Applied Artificial Intelligence

8. Machine Learning Applied Artificial Intelligence 8. Machine Learning Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt University of Applied Sciences 1 Retrospective Natural Language Processing Name

More information

Categorical Data Visualization and Clustering Using Subjective Factors

Categorical Data Visualization and Clustering Using Subjective Factors Categorical Data Visualization and Clustering Using Subjective Factors Chia-Hui Chang and Zhi-Kai Ding Department of Computer Science and Information Engineering, National Central University, Chung-Li,

More information

TIn 1: Lecture 3: Lernziele. Lecture 3 The Belly of the Architect. Basic internal components of the 8086. Pointers and data storage in memory

TIn 1: Lecture 3: Lernziele. Lecture 3 The Belly of the Architect. Basic internal components of the 8086. Pointers and data storage in memory Mitglied der Zürcher Fachhochschule TIn 1: Lecture 3 The Belly of the Architect. Lecture 3: Lernziele Basic internal components of the 8086 Pointers and data storage in memory Architektur 8086 Besteht

More information

Usability Evaluation of Modeling Languages

Usability Evaluation of Modeling Languages Usability Evaluation of Modeling Languages Bearbeitet von Christian Schalles 1. Auflage 2012. Taschenbuch. XXIII, 183 S. Paperback ISBN 978 3 658 00050 9 Format (B x L): 0 x 0 cm Gewicht: 275 g Weitere

More information

Mining the Software Change Repository of a Legacy Telephony System

Mining the Software Change Repository of a Legacy Telephony System Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,

More information

Lernsituation 9. Giving information on the phone. 62 Lernsituation 9 Giving information on the phone

Lernsituation 9. Giving information on the phone. 62 Lernsituation 9 Giving information on the phone Fachkunde 1, Lernfeld 2, Useful Office Vocabulary Lernsituation 9 Giving information on the phone Rolf astian, Managing Director of E Partners KG, recently visited the Promo World Fair in Düsseldorf, an

More information

Information Discovery on Electronic Medical Records

Information Discovery on Electronic Medical Records Information Discovery on Electronic Medical Records Vagelis Hristidis, Fernando Farfán, Redmond P. Burke, MD Anthony F. Rossi, MD Jeffrey A. White, FIU FIU Miami Children s Hospital Miami Children s Hospital

More information

Statistical Feature Selection Techniques for Arabic Text Categorization

Statistical Feature Selection Techniques for Arabic Text Categorization Statistical Feature Selection Techniques for Arabic Text Categorization Rehab M. Duwairi Department of Computer Information Systems Jordan University of Science and Technology Irbid 22110 Jordan Tel. +962-2-7201000

More information

A prototype infrastructure for D Spin Services based on a flexible multilayer architecture

A prototype infrastructure for D Spin Services based on a flexible multilayer architecture A prototype infrastructure for D Spin Services based on a flexible multilayer architecture Volker Boehlke 1,, 1 NLP Group, Department of Computer Science, University of Leipzig, Johanisgasse 26, 04103

More information

Facilitating Business Process Discovery using Email Analysis

Facilitating Business Process Discovery using Email Analysis Facilitating Business Process Discovery using Email Analysis Matin Mavaddat Matin.Mavaddat@live.uwe.ac.uk Stewart Green Stewart.Green Ian Beeson Ian.Beeson Jin Sa Jin.Sa Abstract Extracting business process

More information

Projektgruppe. Categorization of text documents via classification

Projektgruppe. Categorization of text documents via classification Projektgruppe Steffen Beringer Categorization of text documents via classification 4. Juni 2010 Content Motivation Text categorization Classification in the machine learning Document indexing Construction

More information

Intelligent Systems: Three Practical Questions. Carsten Rother

Intelligent Systems: Three Practical Questions. Carsten Rother Intelligent Systems: Three Practical Questions Carsten Rother 04/02/2015 Prüfungsfragen Nur vom zweiten Teil der Vorlesung (Dimitri Schlesinger, Carsten Rother) Drei Typen von Aufgaben: 1) Algorithmen

More information

Elena Chiocchetti & Natascia Ralli (EURAC) Tanja Wissik & Vesna Lušicky (University of Vienna)

Elena Chiocchetti & Natascia Ralli (EURAC) Tanja Wissik & Vesna Lušicky (University of Vienna) Elena Chiocchetti & Natascia Ralli (EURAC) Tanja Wissik & Vesna Lušicky (University of Vienna) VII Conference on Legal Translation, Court Interpreting and Comparative Legilinguistics Poznań, 28-30.06.2013

More information

Doctor of Philosophy in Computer Science

Doctor of Philosophy in Computer Science Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects

More information

AP GERMAN LANGUAGE AND CULTURE EXAM 2015 SCORING GUIDELINES

AP GERMAN LANGUAGE AND CULTURE EXAM 2015 SCORING GUIDELINES AP GERMAN LANGUAGE AND CULTURE EXAM 2015 SCORING GUIDELINES Identical to Scoring Guidelines used for French, Italian, and Spanish Language and Culture Exams Interpersonal Writing: E-mail Reply 5: STRONG

More information

Error Log Processing for Accurate Failure Prediction. Humboldt-Universität zu Berlin

Error Log Processing for Accurate Failure Prediction. Humboldt-Universität zu Berlin Error Log Processing for Accurate Failure Prediction Felix Salfner ICSI Berkeley Steffen Tschirpke Humboldt-Universität zu Berlin Introduction Context of work: Error-based online failure prediction: error

More information

Search and Data Mining: Techniques. Text Mining Anya Yarygina Boris Novikov

Search and Data Mining: Techniques. Text Mining Anya Yarygina Boris Novikov Search and Data Mining: Techniques Text Mining Anya Yarygina Boris Novikov Introduction Generally used to denote any system that analyzes large quantities of natural language text and detects lexical or

More information

Web Document Clustering

Web Document Clustering Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,

More information

Name: Klasse: Datum: A. Was wissen Sie schon? What do you know already from studying Kapitel 1 in Vorsprung? True or false?

Name: Klasse: Datum: A. Was wissen Sie schon? What do you know already from studying Kapitel 1 in Vorsprung? True or false? KAPITEL 1 Jetzt geht s los! Vor dem Anschauen A. Was wissen Sie schon? What do you know already from studying Kapitel 1 in Vorsprung? True or false? 1. Man sagt Hallo in Deutschland. 2. Junge Personen

More information

Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Sushilkumar Kalmegh Associate Professor, Department of Computer Science, Sant Gadge Baba Amravati

More information

A: Ein ganz normaler Prozess B: Best Practices in BPMN 1.x. ITAB / IT Architekturbüro Rüdiger Molle März 2009

A: Ein ganz normaler Prozess B: Best Practices in BPMN 1.x. ITAB / IT Architekturbüro Rüdiger Molle März 2009 A: Ein ganz normaler Prozess B: Best Practices in BPMN 1.x ITAB / IT Architekturbüro Rüdiger Molle März 2009 März 2009 I T A B 2 Lessons learned Beschreibung eines GP durch das Business läßt Fragen der

More information

SVM Based Learning System For Information Extraction

SVM Based Learning System For Information Extraction SVM Based Learning System For Information Extraction Yaoyong Li, Kalina Bontcheva, and Hamish Cunningham Department of Computer Science, The University of Sheffield, Sheffield, S1 4DP, UK {yaoyong,kalina,hamish}@dcs.shef.ac.uk

More information

Interactive Dynamic Information Extraction

Interactive Dynamic Information Extraction Interactive Dynamic Information Extraction Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, and Norbert Reithinger Deutsches Forschungszentrum für Künstliche Intelligenz - DFKI, 66123 Saarbrücken

More information

Web Database Integration

Web Database Integration Web Database Integration Wei Liu School of Information Renmin University of China Beijing, 100872, China gue2@ruc.edu.cn Xiaofeng Meng School of Information Renmin University of China Beijing, 100872,

More information

Distributed Computing and Big Data: Hadoop and MapReduce

Distributed Computing and Big Data: Hadoop and MapReduce Distributed Computing and Big Data: Hadoop and MapReduce Bill Keenan, Director Terry Heinze, Architect Thomson Reuters Research & Development Agenda R&D Overview Hadoop and MapReduce Overview Use Case:

More information

Learning outcomes. Knowledge and understanding. Competence and skills

Learning outcomes. Knowledge and understanding. Competence and skills Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

More information

ida.com excellence in dependable automation

ida.com excellence in dependable automation IEC 61508 Maintenance Status IEC 61508 Maintenance Projekt ist aus dem zulässigen Zeitrahmen gelaufen Viele Baustellen auch durch neue Mitglieder (Frankreich, USA, IEC 61511 Team) Bestehende Anforderungen,

More information

Microsoft Certified IT Professional (MCITP) MCTS: Windows 7, Configuration (070-680)

Microsoft Certified IT Professional (MCITP) MCTS: Windows 7, Configuration (070-680) Microsoft Office Specialist Office 2010 Specialist Expert Master Eines dieser Examen/One of these exams: Eines dieser Examen/One of these exams: Pflichtexamen/Compulsory exam: Word Core (Exam 077-881)

More information

Machine Learning for Naive Bayesian Spam Filter Tokenization

Machine Learning for Naive Bayesian Spam Filter Tokenization Machine Learning for Naive Bayesian Spam Filter Tokenization Michael Bevilacqua-Linn December 20, 2003 Abstract Background Traditional client level spam filters rely on rule based heuristics. While these

More information

Email Spam Detection A Machine Learning Approach

Email Spam Detection A Machine Learning Approach Email Spam Detection A Machine Learning Approach Ge Song, Lauren Steimle ABSTRACT Machine learning is a branch of artificial intelligence concerned with the creation and study of systems that can learn

More information

LINGUISTIC SUPPORT IN "THESIS WRITER": CORPUS-BASED ACADEMIC PHRASEOLOGY IN ENGLISH AND GERMAN

LINGUISTIC SUPPORT IN THESIS WRITER: CORPUS-BASED ACADEMIC PHRASEOLOGY IN ENGLISH AND GERMAN ELN INAUGURAL CONFERENCE, PRAGUE, 7-8 NOVEMBER 2015 EUROPEAN LITERACY NETWORK: RESEARCH AND APPLICATIONS Panel session Recent trends in Bachelor s dissertation/thesis research: foci, methods, approaches

More information

Master of Science in Computer Science

Master of Science in Computer Science Master of Science in Computer Science Background/Rationale The MSCS program aims to provide both breadth and depth of knowledge in the concepts and techniques related to the theory, design, implementation,

More information

Clever Search: A WordNet Based Wrapper for Internet Search Engines

Clever Search: A WordNet Based Wrapper for Internet Search Engines Clever Search: A WordNet Based Wrapper for Internet Search Engines Peter M. Kruse, André Naujoks, Dietmar Rösner, Manuela Kunze Otto-von-Guericke-Universität Magdeburg, Institut für Wissens- und Sprachverarbeitung,

More information

Building a Question Classifier for a TREC-Style Question Answering System

Building a Question Classifier for a TREC-Style Question Answering System Building a Question Classifier for a TREC-Style Question Answering System Richard May & Ari Steinberg Topic: Question Classification We define Question Classification (QC) here to be the task that, given

More information

CENG 734 Advanced Topics in Bioinformatics

CENG 734 Advanced Topics in Bioinformatics CENG 734 Advanced Topics in Bioinformatics Week 9 Text Mining for Bioinformatics: BioCreative II.5 Fall 2010-2011 Quiz #7 1. Draw the decompressed graph for the following graph summary 2. Describe the

More information

Semantic Video Annotation by Mining Association Patterns from Visual and Speech Features

Semantic Video Annotation by Mining Association Patterns from Visual and Speech Features Semantic Video Annotation by Mining Association Patterns from and Speech Features Vincent. S. Tseng, Ja-Hwung Su, Jhih-Hong Huang and Chih-Jen Chen Department of Computer Science and Information Engineering

More information

The Changing Global Egg Industry

The Changing Global Egg Industry Vol. 46 (2), Oct. 2011, Page 3 The Changing Global Egg Industry - The new role of less developed and threshold countries in global egg production and trade 1 - Hans-Wilhelm Windhorst, Vechta, Germany Introduction

More information

PPS Internet-Praktikum. Prof. Bernhard Plattner Institut für Technische Informatik und Kommunikationsnetze (TIK)

PPS Internet-Praktikum. Prof. Bernhard Plattner Institut für Technische Informatik und Kommunikationsnetze (TIK) PPS Internet-Praktikum Prof. Bernhard Plattner Institut für Technische Informatik und Kommunikationsnetze (TIK) September 2011 Zielsetzung Von unserer Webpage: Das Ziel dieser PPS-Veranstaltung ist es,

More information

Themen der Praktikumsnachmittage. PPS Internet-Praktikum. Zielsetzung. Infrastruktur im ETF B5

Themen der Praktikumsnachmittage. PPS Internet-Praktikum. Zielsetzung. Infrastruktur im ETF B5 PPS Internet-Praktikum Prof. Bernhard Plattner Institut für Technische Informatik und Kommunikationsnetze (TIK) Themen der Praktikumsnachmittage Aufbau und Analyse eines kleinen Netzwerks Routing Anwendungen

More information

SPATIAL DATA CLASSIFICATION AND DATA MINING

SPATIAL DATA CLASSIFICATION AND DATA MINING , pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal

More information

Language Interface for an XML. Constructing a Generic Natural. Database. Rohit Paravastu

Language Interface for an XML. Constructing a Generic Natural. Database. Rohit Paravastu Constructing a Generic Natural Language Interface for an XML Database Rohit Paravastu Motivation Ability to communicate with a database in natural language regarded as the ultimate goal for DB query interfaces

More information

Exchange Synchronization AX 2012

Exchange Synchronization AX 2012 Exchange Synchronization AX 2012 Autor... Pascal Gubler Dokument... Exchange Synchronization 2012 (EN) Erstellungsdatum... 25. September 2012 Version... 2 / 17.06.2013 Content 1 PRODUKTBESCHREIBUNG...

More information

Learning outcomes. Knowledge and understanding. Ability and Competences. Evaluation capability and scientific approach

Learning outcomes. Knowledge and understanding. Ability and Competences. Evaluation capability and scientific approach Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

More information

Optimized Scheduling in Real-Time Environments with Column Generation

Optimized Scheduling in Real-Time Environments with Column Generation JG U JOHANNES GUTENBERG UNIVERSITAT 1^2 Optimized Scheduling in Real-Time Environments with Column Generation Dissertation zur Erlangung des Grades,.Doktor der Naturwissenschaften" am Fachbereich Physik,

More information

Combining Global and Personal Anti-Spam Filtering

Combining Global and Personal Anti-Spam Filtering Combining Global and Personal Anti-Spam Filtering Richard Segal IBM Research Hawthorne, NY 10532 Abstract Many of the first successful applications of statistical learning to anti-spam filtering were personalized

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used.

DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used. Data is the new Oil DATA is just like CRUDE. It s valuable, but if unrefined it cannot really be used. Clive Humby "Digitale Informationsspeicher Im Meer der Daten" "Die Menschen produzieren immer mehr

More information

Intinno: A Web Integrated Digital Library and Learning Content Management System

Intinno: A Web Integrated Digital Library and Learning Content Management System Intinno: A Web Integrated Digital Library and Learning Content Management System Synopsis of the Thesis to be submitted in Partial Fulfillment of the Requirements for the Award of the Degree of Master

More information

Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification

Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Performance Analysis of Naive Bayes and J48 Classification Algorithm for Data Classification Tina R. Patil, Mrs. S. S. Sherekar Sant Gadgebaba Amravati University, Amravati tnpatil2@gmail.com, ss_sherekar@rediffmail.com

More information

Adaption of Statistical Email Filtering Techniques

Adaption of Statistical Email Filtering Techniques Adaption of Statistical Email Filtering Techniques David Kohlbrenner IT.com Thomas Jefferson High School for Science and Technology January 25, 2007 Abstract With the rise of the levels of spam, new techniques

More information

Filtering the Web to Feed Data Warehouses

Filtering the Web to Feed Data Warehouses Witold Abramowicz, Pawel Kalczynski and Krzysztof We^cel Filtering the Web to Feed Data Warehouses Springer Table of Contents CHAPTER 1 INTRODUCTION 1 1.1 Information Systems 1 1.2 Information Filtering

More information

Leitfaden für die Antragstellung zur Förderung einer nationalen Biomaterialbankeninitiative

Leitfaden für die Antragstellung zur Förderung einer nationalen Biomaterialbankeninitiative Seite 1 von 8 Leitfaden für die Antragstellung zur Förderung einer nationalen Biomaterialbankeninitiative Anträge zu Biomaterialbanken sind entsprechend den Vorgaben dieses Leitfadens zu erstellen (DIN

More information

KUE-Online A Data-Management-System Supporting the Handling of Design Exercises

KUE-Online A Data-Management-System Supporting the Handling of Design Exercises INTERNATIONAL DESIGN CONFERENCE - DESIGN 2000 Dubrovnik, May 23-26, 2000. KUE-Online A Data-Management-System Supporting the Handling of Design Exercises Dr.-Ing. Michael Muth, Prof. Dr.-Ing. Christian

More information

Spam Filtering using Naïve Bayesian Classification

Spam Filtering using Naïve Bayesian Classification Spam Filtering using Naïve Bayesian Classification Presented by: Samer Younes Outline What is spam anyway? Some statistics Why is Spam a Problem Major Techniques for Classifying Spam Transport Level Filtering

More information

Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System

Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System Athira P. M., Sreeja M. and P. C. Reghuraj Department of Computer Science and Engineering, Government Engineering

More information

Voraussetzungen/ Prerequisites *for English see below*

Voraussetzungen/ Prerequisites *for English see below* English Programme im akademischen Jahr 2013/2014 English Programme in the Academic Year 2013/2014 *for English see below* Im akademischen Jahr 2013/2014 freuen wir uns Ihnen erneut ein Programm mit englischsprachigen

More information

Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below*

Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below* Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below* Im akademischen Jahr 2015/2016 freuen wir uns Ihnen erneut

More information

Information Systems & Semantic Web University of Koblenz Landau, Germany

<is web> Information Systems & Semantic Web University of Koblenz Landau, Germany Information Systems University of Koblenz Landau, Germany Exploiting Spatial Context in Images Using Fuzzy Constraint Reasoning Carsten Saathoff & Agenda Semantic Web: Our Context Knowledge Annotation

More information

Exemplar for Internal Assessment Resource German Level 1. Resource title: Planning a School Exchange

Exemplar for Internal Assessment Resource German Level 1. Resource title: Planning a School Exchange Exemplar for internal assessment resource German 1.5A for Achievement Standard 90887! Exemplar for Internal Assessment Resource German Level 1 Resource title: Planning a School Exchange This exemplar supports

More information

Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below*

Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below* Vorläufiges English Programme im akademischen Jahr 2015/2016 Preliminary English Programme in the Academic Year 2015/2016 *for English see below* Im akademischen Jahr 2015/2016 freuen wir uns Ihnen erneut

More information