The TEXT-TO-ONTO Ontology Learning Environment
|
|
- Wesley Sharp
- 6 years ago
- Views:
Transcription
1 The TEXT-TO-ONTO Ontology Learning Environment Alexander Maedche and Steffen Staab Institute AIFB, University of Karlsruhe, Karlsruhe, Germany Abstract Ontologies have become an important means for structuring information and information systems and, hence, important in knowledge as well as in software engineering. However, there remains the problem of engineering large and adequate ontologies within short time frames in order to keep costs low. For this purpose, we present the TEXT-TO-ONTO Ontology Learning Environment, which is based on a general architecture for discovering conceptual structures and engineering ontologies from text. Our Ontology Learning Environment supports as well the acquisition of conceptual structures as mapping linguistic resources to the acquired structures. 1 Introduction Ontologies 1 have shown their usefulness in application areas such as intelligent information integration, information brokering and natural-language processing, to name but a few. However, their wide-spread usage is still hindered by ontology engineering being rather time-consuming and, hence, expensive. Our system TEXT-TO-ONTO tries to overcome this knowledge acquisition bottleneck through learning and discovering conceptual structures from texts. Natural language texts exhibit morphological, syntactic, semantic, pragmatic and conceptual constraints that interact in order to convey a particular meaning to the reader. Thus, the text transports information to the reader and the reader embeds this information into his background knowledge. Through the understanding of the text data is associated with conceptual structures and new conceptual structures are learned from the interacting constraints given through language. TEXT- TO-ONTO exploits the interacting constraints on the various language levels (from morphology to pragmatics and background knowledge) in order to discover new concepts and stipulate relationships between concepts. The system follows an balanced cooperation approach described in [4], i.e. each modeling task can be done by the user or by a learning tool of the system. This balanced interaction of system and user contributes to the preparation of background knowledge, enhancing the domain knowledge (ontology) and to inspecting the learned knowledge. 1 We restrict our attention in this paper to domain ontologies that describe a particular small model of of the world as relevant to applications, in contrast to top-level ontologies and representational ontologies that aim at the description of generally applicable conceptual structures and meta-structures, respectively, and that are mostly based on philosophical and logical point of views rather than focused on applications.
2 2 TEXT-TO-ONTO Ontology Learning Environment The process of semi-automatic ontology learning from text is embedded in an architecture that comprises several core features described as a kind of pipeline in the following. (cf. the overall schema in Figure 1). Nevertheless, the reader may bear in mind that the overall development of ontologies remains a cyclic process (cf. [1]). In fact, we provide a broad set of interactions such that the engineer may start with primitive methods first. These methods require very little or even no background knowledge, but they may also be restricted to return only simple hints, like term frequencies. While the knowledge model matures during the semi-automatic learning process, the engineer may turn towards more advanced and more knowledge-intensive algorithms, such as our mechanism for discovering generalized non-taxonomic relations described in [2]. natural language texts feed Text & Processing Management (XML tagged) text &selected algorithms Learning & Discovering Algorithms proposes selected text & preprocessing method XMLtagged text against manual model Evaluation Text Processing Server Ontology references models OntoEdit Ontology Modeling Environment Stemming POS tagging chunk parsing Information Extraction... domain lexicon models Lexical DB Figure1. Architecture of the Ontology Learning Environment A comprehensive architecture lays the foundation for acquiring domain ontologies and linguistic resources ([3]). The main components of the architecture are the (i) Text & Processing Management, the (ii) Text Processing Server, (iii) a Lexical Database and Domain Lexicon, a (iv) Learning Module and the (v) Ontology Engineering Environment OntoEdit: Text & Processing Management Component. The ontology engineer the Text & Processing Management Component to select domain texts exploited in the further discovery process. She chooses among a set of text (pre-)processing methods available on the Text Processing Server and among a set of algorithms available at the Learning &
3 Discovering component. The former module returns text that is annotated by XML and this XML-tagged text is fed to the Learning & Discovering component. Text Processing Server. The Text Processing Server may comprise a broad set of different methods. In our case, it contains a shallow text processor based on the core system SMES (Saarbrücken Message Extraction System) [5]. SMES is a system that performs syntactic analysis on natural language documents. In general, the Text Processing Server is organized in modules, such as a tokenizer, morphological and lexical processing, and chunk parsing that use lexical resources to produce mixed syntactic/semantic information. The results of text processing are stored in annotations using XML-tagged text. Figure2. The TEXT-TO-ONTO Ontology Learning Environment Lexical DB & Domain Lexicon. Syntactic processing relies on lexical knowledge. In our system, SMES accesses a lexical database with more than stem entries and more than 12,000 subcategorization frames that are used for lexical analysis and chunk parsing. The domain-specific part of the lexicon (abbreviated domain lexicon ; cf. left lower part of Figure 2) associates word stems with concepts available in the concept taxonomy. Hence, it links syntactic information with semantic knowledge that may be further refined in the ontology.
4 Learning & Discovering component. The Learning & Discovering component various discovering methods on the annotated texts, e.g. term extraction methods for concept acquisition. Our scenario for discovering non-taxonomic relations the learning algorithm for discovering generalized association rules described in [2]. Conceptual structures that exist at learning time (e.g. a concept taxonomy) may be incorporated into the learning algorithms as background knowledge. The evaluation of the applied algorithms such as described in [2] is performed in a submodule based on the results of the learning algorithm. Ontology Engineering Environment. The Ontology Engineering Environment ONTOEDIT, which is a submodule of the Ontology Learning Environment TEXT-TO-ONTO supports the ontology engineer in semi-automatically adding newly discovered conceptual structures to the ontology. A comprehensive description of the ontology engineering system ONTOEDIT and the underlying methodology is given in [8,9]. The screenshot depicted in Figure 2 shows on the left side the object-model backbone of an ontology. In addition to core capabilities for structuring the ontology, the engineering environment provides some additional features for the purpose of documentation, maintenance, and ontology exchange. OntoEdit internally stores modeled ontologies using an XML serialization. 3 Discovering Non-Taxonomic Conceptual Relations from Text using TEXT-TO-ONTO In [2] we describe our approach for discovering non-taxonomic conceptual relations from text faciliting ontology engineering. Building on the user-modeled taxonomic part of the ontology, our approach analyzes domain-specific texts. It shallow text processing methods to identify linguistically related pairs of words, which are mapped to concepts using the domain lexicon. An algorithm for discovering generalized association rules [6] analyzes statistical information about the linguistic output. Thereby, it the background knowledge from the taxonomy in order to propose relations at the appropriate level of abstraction. For instance, the linguistic processing may find that the word costs frequently co-occurs with each of the words hotel, guest house, and youth hostel in sentences such as (1). (1) Costs at the youth hostel amount to $ 20 per night. From this statistical linguistic data our approach derives correlations at the conceptual level, viz. between the concept Costs and the concepts, Hotel, Guest House, and Youth Hostel. The learning algorithm determines support and confidence measures for the relationships between these three pairs, as well as for relationships at higher levels of abstraction, such as between Accommodation and Costs. In a final step, the algorithm determines the level of abstraction most suited to describe the conceptual relationships by pruning appearingly less adequate ones. Here, the relation between Accommodation and Costs may be proposed for inclusion in the ontology. Results of the learning algorithm are visualized as a graph such as depicted on the right side of Figure 2.
5 4 Conclusion We have presented an approach and an implemented system towards learning ontologies from text. Core idea of this approach is to support the knowledge engineer using an balanced cooperative modeling paradigm. We have to emphasize that we do not consider fully automatic ontology acquisition from text as realistic, so we support the knowledge engineer as much as possible with graphical user interfaces and visualization of discovered conceptual structures. The system has been evaluated and applied for building domain ontologies in the tourism domain [7] and the insurance domain. References 1. A. Maedche, H.-P. Schnurr, S. Staab, and R. Studer. Representation language-neutral modeling of ontologies. In U. Frank, editor, Proceedings of the German Workshop Modellierung Koblenz, Germany, April, 5-7, Fölbach-Verlag, A. Maedche and S.Staab. Discovering conceptual relations from text. In W. Horn (ed.): ECAI Proceedings of the 14th European Conference on Artificial Intelligence. IOS Press, Amsterdam, A. Maedche and S. Staab. Semi-automatic engineering of ontologies from text. In Proceedings of the 12th Internal Conference on Software and Knowledge Engineering. Chicago, USA, July, 5-7, KSI, K. Morik. Balanced cooperative modeling. Machine Learning, 11: , G. Neumann, R. Backofen, J. Baur, M. Becker, and C. Braun. An information extraction core system for real world german text processing. In ANLP 97 Proceedings of the Conference on Applied Natural Language Processing, pages , Washington, USA, R. Srikant and R. Agrawal. Mining generalized association rules. In Proc. of VLDB 95, pages , S. Staab, C. Braun, I. Bruder, A. Düsterhöft, A. Heuer, M. Klettke, G. Neumann, B. Prager, J. Pretzel, H.-P. Schnurr, R. Studer, H. Uszkoreit, and B. Wrenger. GETESS searching the web exploiting german texts. In CIA 99 Proceedings of the 3rd Workshop on Cooperative Information Agents, LNAI 1652, pages , Berlin, Springer. 8. S. Staab and A. Maedche. Axioms are Objects, too - Ontology Engineering beyond the modeling of Concepts and Relations. Technical Report 400, Institute AIFB, Karlsruhe University, S. Staab and A. Maedche. Ontology engineering beyond the modeling of concepts and relations. In A. Gomez-Perez (ed.): Proceedings of the ECAI 2000 Workshop on Application of Ontologies and Problem-Solving Methods. IOS Press, Amsterdam, 2000.
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments
Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,
More informationA Domain Ontology Development Environment Using a MRD and Text Corpus
A Domain Ontology Development Environment Using a MRD and Text Corpus Naomi Nakaya 1 and Masaki Kurematsu 2 and Takahira Yamaguchi 1 1 Faculty of Information, Shizuoka University 3-5-1 Johoku Hamamatsu
More informationAQUA: An Ontology-Driven Question Answering System
AQUA: An Ontology-Driven Question Answering System Maria Vargas-Vera, Enrico Motta and John Domingue Knowledge Media Institute (KMI) The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom.
More informationOntologies vs. classification systems
Ontologies vs. classification systems Bodil Nistrup Madsen Copenhagen Business School Copenhagen, Denmark bnm.isv@cbs.dk Hanne Erdman Thomsen Copenhagen Business School Copenhagen, Denmark het.isv@cbs.dk
More informationCREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT
CREATING SHARABLE LEARNING OBJECTS FROM EXISTING DIGITAL COURSE CONTENT Rajendra G. Singh Margaret Bernard Ross Gardler rajsingh@tstt.net.tt mbernard@fsa.uwi.tt rgardler@saafe.org Department of Mathematics
More informationApplications of memory-based natural language processing
Applications of memory-based natural language processing Antal van den Bosch and Roser Morante ILK Research Group Tilburg University Prague, June 24, 2007 Current ILK members Principal investigator: Antal
More informationWhat s in a Step? Toward General, Abstract Representations of Tutoring System Log Data
What s in a Step? Toward General, Abstract Representations of Tutoring System Log Data Kurt VanLehn 1, Kenneth R. Koedinger 2, Alida Skogsholm 2, Adaeze Nwaigwe 2, Robert G.M. Hausmann 1, Anders Weinstein
More informationLinking Task: Identifying authors and book titles in verbose queries
Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationAutomating the E-learning Personalization
Automating the E-learning Personalization Fathi Essalmi 1, Leila Jemni Ben Ayed 1, Mohamed Jemni 1, Kinshuk 2, and Sabine Graf 2 1 The Research Laboratory of Technologies of Information and Communication
More informationAgent-Based Software Engineering
Agent-Based Software Engineering Learning Guide Information for Students 1. Description Grade Module Máster Universitario en Ingeniería de Software - European Master on Software Engineering Advanced Software
More informationComputerized Adaptive Psychological Testing A Personalisation Perspective
Psychology and the internet: An European Perspective Computerized Adaptive Psychological Testing A Personalisation Perspective Mykola Pechenizkiy mpechen@cc.jyu.fi Introduction Mixed Model of IRT and ES
More informationAn Open Framework for Integrated Qualification Management Portals
An Open Framework for Integrated Qualification Management Portals Michael Fuchs, Claudio Muscogiuri, Claudia Niederée, Matthias Hemmje FhG IPSI D-64293 Darmstadt, Germany {fuchs,musco,niederee,hemmje}@ipsi.fhg.de
More informationAUTHORING E-LEARNING CONTENT TRENDS AND SOLUTIONS
AUTHORING E-LEARNING CONTENT TRENDS AND SOLUTIONS Danail Dochev 1, Radoslav Pavlov 2 1 Institute of Information Technologies Bulgarian Academy of Sciences Bulgaria, Sofia 1113, Acad. Bonchev str., Bl.
More informationVisual CP Representation of Knowledge
Visual CP Representation of Knowledge Heather D. Pfeiffer and Roger T. Hartley Department of Computer Science New Mexico State University Las Cruces, NM 88003-8001, USA email: hdp@cs.nmsu.edu and rth@cs.nmsu.edu
More informationRule discovery in Web-based educational systems using Grammar-Based Genetic Programming
Data Mining VI 205 Rule discovery in Web-based educational systems using Grammar-Based Genetic Programming C. Romero, S. Ventura, C. Hervás & P. González Universidad de Córdoba, Campus Universitario de
More informationLearning Methods for Fuzzy Systems
Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8
More informationAn Interactive Intelligent Language Tutor Over The Internet
An Interactive Intelligent Language Tutor Over The Internet Trude Heift Linguistics Department and Language Learning Centre Simon Fraser University, B.C. Canada V5A1S6 E-mail: heift@sfu.ca Abstract: This
More informationDeveloping a TT-MCTAG for German with an RCG-based Parser
Developing a TT-MCTAG for German with an RCG-based Parser Laura Kallmeyer, Timm Lichte, Wolfgang Maier, Yannick Parmentier, Johannes Dellert University of Tübingen, Germany CNRS-LORIA, France LREC 2008,
More informationCollaborative Problem Solving using an Open Modeling Environment
Collaborative Problem Solving using an Open Modeling Environment C. Fidas 1, V. Komis 1, N.M. Avouris 1, A Dimitracopoulou 2 1 University of Patras, Patras, Greece 2 University of the Aegean, Rhodes, Greece
More informationOn-Line Data Analytics
International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob
More informationEvaluation of Usage Patterns for Web-based Educational Systems using Web Mining
Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl
More informationEvaluation of Usage Patterns for Web-based Educational Systems using Web Mining
Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl
More informationExploiting Phrasal Lexica and Additional Morpho-syntactic Language Resources for Statistical Machine Translation with Scarce Training Data
Exploiting Phrasal Lexica and Additional Morpho-syntactic Language Resources for Statistical Machine Translation with Scarce Training Data Maja Popović and Hermann Ney Lehrstuhl für Informatik VI, Computer
More informationMASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE
Master of Science (M.S.) Major in Computer Science 1 MASTER OF SCIENCE (M.S.) MAJOR IN COMPUTER SCIENCE Major Program The programs in computer science are designed to prepare students for doctoral research,
More informationSemi-supervised methods of text processing, and an application to medical concept extraction. Yacine Jernite Text-as-Data series September 17.
Semi-supervised methods of text processing, and an application to medical concept extraction Yacine Jernite Text-as-Data series September 17. 2015 What do we want from text? 1. Extract information 2. Link
More informationNotes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1
Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial
More informationIntra-talker Variation: Audience Design Factors Affecting Lexical Selections
Tyler Perrachione LING 451-0 Proseminar in Sound Structure Prof. A. Bradlow 17 March 2006 Intra-talker Variation: Audience Design Factors Affecting Lexical Selections Abstract Although the acoustic and
More informationReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology
ReinForest: Multi-Domain Dialogue Management Using Hierarchical Policies and Knowledge Ontology Tiancheng Zhao CMU-LTI-16-006 Language Technologies Institute School of Computer Science Carnegie Mellon
More informationParsing of part-of-speech tagged Assamese Texts
IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 28 Parsing of part-of-speech tagged Assamese Texts Mirzanur Rahman 1, Sufal
More informationEfficient Use of Space Over Time Deployment of the MoreSpace Tool
Efficient Use of Space Over Time Deployment of the MoreSpace Tool Štefan Emrich Dietmar Wiegand Felix Breitenecker Marijana Srećković Alexandra Kovacs Shabnam Tauböck Martin Bruckner Benjamin Rozsenich
More informationSINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)
SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,
More informationA Case Study: News Classification Based on Term Frequency
A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center
More informationPredicting Students Performance with SimStudent: Learning Cognitive Skills from Observation
School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University Year 2007 Predicting Students Performance with SimStudent: Learning Cognitive Skills from Observation Noboru Matsuda
More informationKnowledge-Based - Systems
Knowledge-Based - Systems ; Rajendra Arvind Akerkar Chairman, Technomathematics Research Foundation and Senior Researcher, Western Norway Research institute Priti Srinivas Sajja Sardar Patel University
More informationEnhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities
Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Yoav Goldberg Reut Tsarfaty Meni Adler Michael Elhadad Ben Gurion
More informationOnline Marking of Essay-type Assignments
Online Marking of Essay-type Assignments Eva Heinrich, Yuanzhi Wang Institute of Information Sciences and Technology Massey University Palmerston North, New Zealand E.Heinrich@massey.ac.nz, yuanzhi_wang@yahoo.com
More informationCommunity-oriented Course Authoring to Support Topic-based Student Modeling
Community-oriented Course Authoring to Support Topic-based Student Modeling Sergey Sosnovsky, Michael Yudelson, Peter Brusilovsky School of Information Sciences, University of Pittsburgh, USA {sas15, mvy3,
More informationDYNAMIC ADAPTIVE HYPERMEDIA SYSTEMS FOR E-LEARNING
University of Craiova, Romania Université de Technologie de Compiègne, France Ph.D. Thesis - Abstract - DYNAMIC ADAPTIVE HYPERMEDIA SYSTEMS FOR E-LEARNING Elvira POPESCU Advisors: Prof. Vladimir RĂSVAN
More informationTHE VERB ARGUMENT BROWSER
THE VERB ARGUMENT BROWSER Bálint Sass sass.balint@itk.ppke.hu Péter Pázmány Catholic University, Budapest, Hungary 11 th International Conference on Text, Speech and Dialog 8-12 September 2008, Brno PREVIEW
More informationA Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many
Schmidt 1 Eric Schmidt Prof. Suzanne Flynn Linguistic Study of Bilingualism December 13, 2013 A Minimalist Approach to Code-Switching In the field of linguistics, the topic of bilingualism is a broad one.
More informationProduct Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments
Product Feature-based Ratings foropinionsummarization of E-Commerce Feedback Comments Vijayshri Ramkrishna Ingale PG Student, Department of Computer Engineering JSPM s Imperial College of Engineering &
More informationA MULTI-AGENT SYSTEM FOR A DISTANCE SUPPORT IN EDUCATIONAL ROBOTICS
A MULTI-AGENT SYSTEM FOR A DISTANCE SUPPORT IN EDUCATIONAL ROBOTICS Sébastien GEORGE Christophe DESPRES Laboratoire d Informatique de l Université du Maine Avenue René Laennec, 72085 Le Mans Cedex 9, France
More informationCOMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR
COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR ROLAND HAUSSER Institut für Deutsche Philologie Ludwig-Maximilians Universität München München, West Germany 1. CHOICE OF A PRIMITIVE OPERATION The
More informationDevelopment of an IT Curriculum. Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008
Development of an IT Curriculum Dr. Jochen Koubek Humboldt-Universität zu Berlin Technische Universität Berlin 2008 Curriculum A curriculum consists of everything that promotes learners intellectual, personal,
More informationObjectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition
Chapter 2: The Representation of Knowledge Expert Systems: Principles and Programming, Fourth Edition Objectives Introduce the study of logic Learn the difference between formal logic and informal logic
More informationContent-free collaborative learning modeling using data mining
User Model User-Adap Inter DOI 10.1007/s11257-010-9095-z ORIGINAL PAPER Content-free collaborative learning modeling using data mining Antonio R. Anaya Jesús G. Boticario Received: 23 April 2010 / Accepted
More information21 st Century Skills and New Models of Assessment for a Global Workplace
21 st Century Skills and New Models of Assessment for a Global Workplace Chris Dede Harvard Graduate School of Education Chris_Dede@harvard.edu www.gse.harvard.edu/~dedech Partnership for 21 st Century
More informationData Integration through Clustering and Finding Statistical Relations - Validation of Approach
Data Integration through Clustering and Finding Statistical Relations - Validation of Approach Marek Jaszuk, Teresa Mroczek, and Barbara Fryc University of Information Technology and Management, ul. Sucharskiego
More informationThe Verbmobil Semantic Database. Humboldt{Univ. zu Berlin. Computerlinguistik. Abstract
The Verbmobil Semantic Database Karsten L. Worm Univ. des Saarlandes Computerlinguistik Postfach 15 11 50 D{66041 Saarbrucken Germany worm@coli.uni-sb.de Johannes Heinecke Humboldt{Univ. zu Berlin Computerlinguistik
More informationUSER ADAPTATION IN E-LEARNING ENVIRONMENTS
USER ADAPTATION IN E-LEARNING ENVIRONMENTS Paraskevi Tzouveli Image, Video and Multimedia Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens tpar@image.
More informationBeyond the Pipeline: Discrete Optimization in NLP
Beyond the Pipeline: Discrete Optimization in NLP Tomasz Marciniak and Michael Strube EML Research ggmbh Schloss-Wolfsbrunnenweg 33 69118 Heidelberg, Germany http://www.eml-research.de/nlp Abstract We
More informationA Comparison of Two Text Representations for Sentiment Analysis
010 International Conference on Computer Application and System Modeling (ICCASM 010) A Comparison of Two Text Representations for Sentiment Analysis Jianxiong Wang School of Computer Science & Educational
More informationWord Segmentation of Off-line Handwritten Documents
Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department
More informationECE-492 SENIOR ADVANCED DESIGN PROJECT
ECE-492 SENIOR ADVANCED DESIGN PROJECT Meeting #3 1 ECE-492 Meeting#3 Q1: Who is not on a team? Q2: Which students/teams still did not select a topic? 2 ENGINEERING DESIGN You have studied a great deal
More informationCONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS
CONCEPT MAPS AS A DEVICE FOR LEARNING DATABASE CONCEPTS Pirjo Moen Department of Computer Science P.O. Box 68 FI-00014 University of Helsinki pirjo.moen@cs.helsinki.fi http://www.cs.helsinki.fi/pirjo.moen
More informationThe Strong Minimalist Thesis and Bounded Optimality
The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this
More informationResearch directions on Semantic Web and education
Scientia Interdisciplinary Studies in Computer Science 19(1): 60-67, January/June 2008 2008 by Unisinos Research directions on Semantic Web and education Ig Ibert Bittencourt 1,2, Seiji Isotani 3, Evandro
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationDistant Supervised Relation Extraction with Wikipedia and Freebase
Distant Supervised Relation Extraction with Wikipedia and Freebase Marcel Ackermann TU Darmstadt ackermann@tk.informatik.tu-darmstadt.de Abstract In this paper we discuss a new approach to extract relational
More informationPhonological and Phonetic Representations: The Case of Neutralization
Phonological and Phonetic Representations: The Case of Neutralization Allard Jongman University of Kansas 1. Introduction The present paper focuses on the phenomenon of phonological neutralization to consider
More informationSEMAFOR: Frame Argument Resolution with Log-Linear Models
SEMAFOR: Frame Argument Resolution with Log-Linear Models Desai Chen or, The Case of the Missing Arguments Nathan Schneider SemEval July 16, 2010 Dipanjan Das School of Computer Science Carnegie Mellon
More informationSome Principles of Automated Natural Language Information Extraction
Some Principles of Automated Natural Language Information Extraction Gregers Koch Department of Computer Science, Copenhagen University DIKU, Universitetsparken 1, DK-2100 Copenhagen, Denmark Abstract
More informationLessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities
Lessons from a Massive Open Online Course (MOOC) on Natural Language Processing for Digital Humanities Simon Clematide, Isabel Meraner, Noah Bubenhofer, Martin Volk Institute of Computational Linguistics
More informationSoftware Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
More informationKnowledge Elicitation Tool Classification. Janet E. Burge. Artificial Intelligence Research Group. Worcester Polytechnic Institute
Page 1 of 28 Knowledge Elicitation Tool Classification Janet E. Burge Artificial Intelligence Research Group Worcester Polytechnic Institute Knowledge Elicitation Methods * KE Methods by Interaction Type
More informationGetting the Story Right: Making Computer-Generated Stories More Entertaining
Getting the Story Right: Making Computer-Generated Stories More Entertaining K. Oinonen, M. Theune, A. Nijholt, and D. Heylen University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands {k.oinonen
More informationCross Language Information Retrieval
Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................
More informationOperational Knowledge Management: a way to manage competence
Operational Knowledge Management: a way to manage competence Giulio Valente Dipartimento di Informatica Universita di Torino Torino (ITALY) e-mail: valenteg@di.unito.it Alessandro Rigallo Telecom Italia
More informationManaging Experience for Process Improvement in Manufacturing
Managing Experience for Process Improvement in Manufacturing Radhika Selvamani B., Deepak Khemani A.I. & D.B. Lab, Dept. of Computer Science & Engineering I.I.T.Madras, India khemani@iitm.ac.in bradhika@peacock.iitm.ernet.in
More informationFUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria
FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate
More informationP. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou, C. Skourlas, J. Varnas
Exploiting Distance Learning Methods and Multimediaenhanced instructional content to support IT Curricula in Greek Technological Educational Institutes P. Belsis, C. Sgouropoulou, K. Sfikas, G. Pantziou,
More informationModeling full form lexica for Arabic
Modeling full form lexica for Arabic Susanne Alt Amine Akrout Atilf-CNRS Laurent Romary Loria-CNRS Objectives Presentation of the current standardization activity in the domain of lexical data modeling
More informationA Corpus-based Evaluation of a Domain-specific Text to Knowledge Mapping Prototype
A Corpus-based Evaluation of a Domain-specific Text to Knowledge Mapping Prototype Rushdi Shams Department of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Bangladesh
More informationThe Language of Football England vs. Germany (working title) by Elmar Thalhammer. Abstract
The Language of Football England vs. Germany (working title) by Elmar Thalhammer Abstract As opposed to about fifteen years ago, football has now become a socially acceptable phenomenon in both Germany
More informationhave to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,
A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994
More informationUsing Virtual Manipulatives to Support Teaching and Learning Mathematics
Using Virtual Manipulatives to Support Teaching and Learning Mathematics Joel Duffin Abstract The National Library of Virtual Manipulatives (NLVM) is a free website containing over 110 interactive online
More informationNatural Language Processing. George Konidaris
Natural Language Processing George Konidaris gdk@cs.brown.edu Fall 2017 Natural Language Processing Understanding spoken/written sentences in a natural language. Major area of research in AI. Why? Humans
More informationLearning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models
Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za
More informationEssentials of Rapid elearning (REL) Design
Essentials of Rapid elearning (REL) Design Course Description In this exclusive 2-day, in person training, you ll experience the hands-on practice and coaching you need to refine and enhance your understanding
More informationDifferent Requirements Gathering Techniques and Issues. Javaria Mushtaq
835 Different Requirements Gathering Techniques and Issues Javaria Mushtaq Abstract- Project management is now becoming a very important part of our software industries. To handle projects with success
More informationModeling function word errors in DNN-HMM based LVCSR systems
Modeling function word errors in DNN-HMM based LVCSR systems Melvin Jose Johnson Premkumar, Ankur Bapna and Sree Avinash Parchuri Department of Computer Science Department of Electrical Engineering Stanford
More informationPatterns for Adaptive Web-based Educational Systems
Patterns for Adaptive Web-based Educational Systems Aimilia Tzanavari, Paris Avgeriou and Dimitrios Vogiatzis University of Cyprus Department of Computer Science 75 Kallipoleos St, P.O. Box 20537, CY-1678
More informationThe MEANING Multilingual Central Repository
The MEANING Multilingual Central Repository J. Atserias, L. Villarejo, G. Rigau, E. Agirre, J. Carroll, B. Magnini, P. Vossen January 27, 2004 http://www.lsi.upc.es/ nlp/meaning Jordi Atserias TALP Index
More informationSpecification of the Verity Learning Companion and Self-Assessment Tool
Specification of the Verity Learning Companion and Self-Assessment Tool Sergiu Dascalu* Daniela Saru** Ryan Simpson* Justin Bradley* Eva Sarwar* Joohoon Oh* * Department of Computer Science ** Dept. of
More informationIntroduction of Open-Source e-learning Environment and Resources: A Novel Approach for Secondary Schools in Tanzania
Introduction of Open-Source e- Environment and Resources: A Novel Approach for Secondary Schools in Tanzania S. K. Lujara, M. M. Kissaka, L. Trojer and N. H. Mvungi Abstract The concept of e- is now emerging
More informationA 3D SIMULATION GAME TO PRESENT CURTAIN WALL SYSTEMS IN ARCHITECTURAL EDUCATION
A 3D SIMULATION GAME TO PRESENT CURTAIN WALL SYSTEMS IN ARCHITECTURAL EDUCATION Eray ŞAHBAZ* & Fuat FİDAN** *Eray ŞAHBAZ, PhD, Department of Architecture, Karabuk University, Karabuk, Turkey, E-Mail: eraysahbaz@karabuk.edu.tr
More informationGuide to Teaching Computer Science
Guide to Teaching Computer Science Orit Hazzan Tami Lapidot Noa Ragonis Guide to Teaching Computer Science An Activity-Based Approach Dr. Orit Hazzan Associate Professor Technion - Israel Institute of
More informationDesigning a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses
Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,
More informationLanguage Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus
Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,
More informationData Fusion Models in WSNs: Comparison and Analysis
Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,
More informationIntroduction to HPSG. Introduction. Historical Overview. The HPSG architecture. Signature. Linguistic Objects. Descriptions.
to as a linguistic theory to to a member of the family of linguistic frameworks that are called generative grammars a grammar which is formalized to a high degree and thus makes exact predictions about
More informationBYLINE [Heng Ji, Computer Science Department, New York University,
INFORMATION EXTRACTION BYLINE [Heng Ji, Computer Science Department, New York University, hengji@cs.nyu.edu] SYNONYMS NONE DEFINITION Information Extraction (IE) is a task of extracting pre-specified types
More informationCOMPUTER-AIDED DESIGN TOOLS THAT ADAPT
COMPUTER-AIDED DESIGN TOOLS THAT ADAPT WEI PENG CSIRO ICT Centre, Australia and JOHN S GERO Krasnow Institute for Advanced Study, USA 1. Introduction Abstract. This paper describes an approach that enables
More informationA Framework for Customizable Generation of Hypertext Presentations
A Framework for Customizable Generation of Hypertext Presentations Benoit Lavoie and Owen Rambow CoGenTex, Inc. 840 Hanshaw Road, Ithaca, NY 14850, USA benoit, owen~cogentex, com Abstract In this paper,
More informationA Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique
A Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique Hiromi Ishizaki 1, Susan C. Herring 2, Yasuhiro Takishima 1 1 KDDI R&D Laboratories, Inc. 2 Indiana University
More information1. Introduction. 2. The OMBI database editor
OMBI bilingual lexical resources: Arabic-Dutch / Dutch-Arabic Carole Tiberius, Anna Aalstein, Instituut voor Nederlandse Lexicologie Jan Hoogland, Nederlands Instituut in Marokko (NIMAR) In this paper
More informationPython Machine Learning
Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled
More informationTwitter Sentiment Classification on Sanders Data using Hybrid Approach
IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 4, Ver. I (July Aug. 2015), PP 118-123 www.iosrjournals.org Twitter Sentiment Classification on Sanders
More informationRunning Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY
SCIT Model 1 Running Head: STUDENT CENTRIC INTEGRATED TECHNOLOGY Instructional Design Based on Student Centric Integrated Technology Model Robert Newbury, MS December, 2008 SCIT Model 2 Abstract The ADDIE
More information