1 Alexander Gelbukh Moscow, Russia. 2 Mexico 3 Computing Research Center (CIC), Mexico

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1 Alexander Gelbukh Moscow, Russia Slide 2 2 Mexico Slide 3 3 Computing Research Center (CIC), Mexico Slide 4 4 Chung-Ang University, Korea Electronic Commerce and Internet Application Lab Slide 5 5 Special Topics in Computer Science The Art of Information Retrieval Alexander Gelbukh www.Gelbukh.com Slide 6 6 Information Retrieval In a huge amount of poorly structured information find the information that you need when you dont know exactly what you need or cant explain it The Web User information need Ranking Slide 7 7 Slide 8 8 Slide 9 9 Information Retrieval In a huge amount of poorly structured information find the information that you need when you dont know exactly what you need or cant explain it The Web User information need Ranking Slide 10 10 Importance Knowledge: the main treasure of man Web: Repository? Cemetery of information! Natural language and multimedia information oPoorly structured, badly written Corporate and organizational document bases oSenate speeches: Mexico oMedical data collections oCorporate memory. Microsoft knowledge base Future: data explosion increasing importance Slide 11 11 Perspectives Corporations: corporate databases Organizations: document bases Government oEuropean Union multilingual problem oThe same in Asia Academy oLots of open research topics oWeb topics oComputational Linguistics topics oIntelligent technologies, AI Slide 12 12 Textbook http://sunsite.dcc.uchile.cl/irbook/ Slide 13 13 Contents 1.Introduction 2.Modeling 3.Retrieval Evaluation 4.Query Languages 5.Query Operations 6.Text and Multimedia Languages and Properties 7.Text Operations 8.Indexing and Searching 9.Parallel and Distributed IR 10.User Interfaces and Visualization 11.Multimedia IR: Models and Languages 12.Multimedia IR: Indexing and Searching 13.Searching the Web 14.Libraries and Bibliographical Systems 15.Digital Libraries Slide 14 14 Calendar 1.September 18Chapter 1 Introduction 2.September 25Chapter 2 Modeling 3.October 2Chapter 3 Retrieval Evaluation 4.October 9Chapter 4 Query Languages 5.October 16Chapter 5 Query Operations October 23 midterm exam 6.October 30Chapter 6 Text and Multimedia Languages... 7.November 6Chapter 7 Text Operations 8.November 13Chapter 8 Indexing and Searching 9.November 20Chapter 10 User Interfaces and Visualization 10.November 27Chapter 13 Searching the Web 11.December 4Chapter 14 Libraries and Bibliographical Systems 12.December 11Chapter 15 Digital Libraries December final exam Slide 15 15 Class structure Main course: Information Retrieval Discussion of previous chapter. Questions I briefly present a new chapter Research seminar: Natural Language Processing Discussion of previous paper. Questions. oIdentification of possible research topics Presentation of a new paper or current work Discussion and questions Goal: publications! Slide 16 16 Natural Language Processing Research Seminar Slide 17 17 What CL is about Computers to process natural language text Understand Generate Search Organize Translate Useful in IR Slide 18 18 Methods No: text as a stream of letters oBrute force statistics oSimplified heuristics (ex.: Porter) Yes: attention to language rules oLinguistically motivated approaches oKnowledge-based approaches oCorpus-based approaches Slide 19 19 What IR is about Classical IR: find words? Concepts! Question answering Summarization Clustering Take language seriously Slide 20 20 Text representations for IR Represent the retrieval features oStrings stems (lexemes), synsets, phrases. oWomen woman, lady, female oOld men and women old woman Structured representation of text oNetwork of related events and entities oEnables logical inference Slide 21 21 CL tasks useful in IR Morphology (stemming) POS / Word dense disambiguation Word relatedness Anaphora resolution Parsing and semantics (phrase search) Synonymic rephrasing Translation etc Each one a whole science in itself Slide 22 22 Morphology Q: pig T: piggish Simple: stemming opiggish pig- Lexeme: set of word forms osame stem can give different words opigment not pig; piny pine, not pin Dictionary/corpus-based methods oLearning; dictionary management Slide 23 23 Part of Speech Disambiguation Q: oil well T: He did very well Q: what is an are? T: They are nice Important for English, Chinese. Less important for other types Perhaps not so helpful directly, but is necessary for most other tasks Usually statistical / heuristic methods Slide 24 24 Word Sense Disambiguation Q: bank account T: on the beautiful banks of Han river... bill: document, banknote, law, ax, peak, Gates... Very frequent, almost any word in text Statistical & dictionary methods International competitions Slide 25 25 Word relatedness Q: female T: woman (women) oSynonyms. Subtypes/super-types oDictionaries. WordNet. Similarity. Lesk. Q: Korea T: Seoul oOther linguistic relationships (e.g., part) oReal-world relationships (facts) Q: Clinton T: Lewinsky oStatistical co-occurrence (MI) Slide 26 26 Anaphora resolution Q: Awards of Prof. Han T: Prof. Han said... He did... IBM awarded him... oFrequency oPhrases, co-occurrence, summarization, inference, translation Heuristic (Mitkov) and knowledge- based methods Other types of co-reference Slide 27 27 Parsing, semantics Q: Awards of Prof. Han T1: Prof. Han among many other prizes has several IBM awards T2: Mr. Kang has an award Prof. Han does not know of Understanding of text oRich structured representation Better phrase search; question answering, summarization,... Slide 28 28 Synonymic rephrasing, reasoning Q: experienced computer scientists T: Prof. Han has been programming for many years and awarded an IBM award Requires good syntactic and semantic analysis Knowledge-based methods Slide 29 29 Multilingual access Q: T: We sell excellent yoghurt. . Se vende rico yogur. oSearch multilingual collections Europe: dozens of official languages of EU oIf you dont know how to say it in English Dictionaries, bilingual corpora,... Slide 30 30 Tasks are entangled Many of CL tasks require other tasks oMorphology syntax semantics Many CL tasks form circles oparsing WSD parsing oI see a wild cat with a telescope (tripod?) Can be done quick-and-dirty (?) oFighting for last %s oZipf law: 20% of men drink 80% of beer Slide 31 31 Tools and infrastructure Analysis tools oTasks, methods Dictionaries and grammars oTypes, structure oAutomatic acquisition Corpora oCorpora analysis tools and methods Slide 32 32 Possible tasks WSD to help IR Clustering + summarization in IR results Anaphora and coreference resolution to help IR Multilingual IR Applications to Korean... a lot of others Slide 33 33 Reading Textbooks oManning & Schtze, Allen, Jurafsky, Hausser,... CICLing proceedings Computational Linguistics Google, ResearchIndex Slide 34 34 Questions Who expects to publish? Who will make a presentation at the next seminar? Slide 35 35 Thank you! Till September 18