[ 921INSYIREK13 ] KV (*)Information Retrieval and Extraction

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Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Birgit Pröll 2 SSt Johannes Kepler Universität Linz
Quellcurriculum Masterstudium Computer Science 2021W
Ziele (*)Students have competence in fundamentals and technologies of (1) Information Retrieval, comprising representation, storage and retrieval of textual unstructured information, (2) Information Extraction, comprising Named Entity Recognition (NER) and Natural Language Processing/Understanding (NLP/NLU), and (3) Dialogue Systems. They are able to implement and evaluate applications in these fields and have knowledge about related fields and current research topics.
Lehrinhalte (*)1) Fundamentals and concepts of traditional information retrieval (IR)

  • Document representation: indexing, weighting (tf*idf)
  • IR models: boolsch, vector space etc.
  • Architectures and (natural language) user interfaces
  • Evaluation in IR: recall, precision
  • Related concepts: string similarity, thesaurus, classification, relevance feedback, query expansion, context-based IR
  • IR tools and applications

2) Fundamentals and concepts of information extraction (IE)

  • IE types: NER, relation extraction etc.
  • IE approaches and architectures: focusing on knowledge/rule-based approaches
  • natural language processing/understanding (NLP/NLU)
  • evaluation in IE
  • IE tools and applications

3)Fundamentals and concepts of dialogue systems (DS)

  • properties of human conversation, dialogue structure and state
  • DS approaches, architectures, end evaluation
  • search and extraction in dialogue systems
  • DS tools and applications

4) Selected topics and current research

  • information filtering & recommender systems
  • text recognition, optical character recognition (OCR)
  • multilingual/crosslingual IR
  • text summarization
  • natural language generation (NLG) etc
Beurteilungskriterien (*)exercises, exam, in-class contribution
Lehrmethoden (*)slide presentation (slides on Moodle), exercises (group work)
Abhaltungssprache Englisch
Literatur (*)
  • Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Addison Wesley 2010
  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze: Introduction to Information Retrieval, Cambridge University Press 2008
  • W. Bruce Croft, Donald Metzler, Trevor Strohman: Search Engines – Information Retrieval in Practice, Pearson 2009
Lehrinhalte wechselnd? Nein
Äquivalenzen (*)in collaboration with 921INSYASWK13: KV Accessible Software and Web Design (1,5 ECTS) equivalent to
INMIPKVKCSY: KV Knowledge-centered Systems (4,5 ECTS)
Teilungsziffer -
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