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Overview of IR Research

This overview explores the field of Information Retrieval (IR), its history, connection with related areas like Machine Learning and Library & Information Science, and its applications in various domains. It delves into the evolution of IR research, its relationship with Machine Learning, Library & Information Science, and Software Engineering, and highlights the importance of scalability and data engineering in developing optimal IR systems. The narrative also examines the broad spectrum of IR applications and publication venues, signaling a future where search is ubiquitous and essential.

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Overview of IR Research

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  1. Overview of IR Research ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign

  2. What is Information Retrieval (IR)? • Salton’s definition (Salton 68): “information retrieval is a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information” • Information: mostly text, but can be anything (e.g., multimedia) • Retrieval: • Narrow sense: search/querying • Broad sense: filtering, classification, summarization, ... • In more general terms • Information access • Information seeking • Help people manage and make use of all kinds of information

  3. Who are working on IR? (IR and Related Areas) Applications Models Applications Web, Bioinformatics… Machine Learning Pattern Recognition Data Mining Human-Computer Interaction Library & Info Science Statistics Optimization Information Retrieval Computer Vision Databases Natural Language Processing Software engineering Computer systems Algorithms Systems

  4. IR and NLP • The two fields were closely related from day one, but somewhat disconnected later when NLP focused more on cognitive and symbolic approaches, while IR focused more on pure statistical approaches • Most recently the two fields regained close interactions • More complex retrieval tasks (question answering, opinons) • More scalable/robust NLP techniques (parsing, extraction) • IR researchers pioneered statistical approaches to NLP in 1950’s (e.g., H. P. Luhn), which only became popular in 1990’s among NLP researchers

  5. IR and Databases • “Sibling” fields, but they didn’t get along with each other well • IR and DB share many common tasks, but the differences in the form of data and nature of queries are large enough to separate the two fields in most of the history • Major differences in data, user, query, what counts as answers: DB  efficiency; IR  effectiveness • The two fields are now getting closer and closer now (DB researchers realized the importance of 80% unstructured data, and IR researchers realized the importance of semantic search)

  6. IR and Machine Learning • IR as a subfield of AI (IR=intelligent text access)? • AI is too big to have a coherent community (e.g., ML, NLP, Computer Vision all “spin off”) • IR researchers did machine learning as early as in 1960’s (Rocchio 1965, relevance feedback), but supervised learning didn’t get popular in IR until in early 1990’s when text categorization started getting a lot of attention • Lack of training data for search (no large-scale online system, users don’t like to make effort on judgments) • Learning-based approach didn’t prevail for ad hoc retrieval • Machine learning is now very important for IR

  7. IR and Library & Information Science • Inseparable from day one (“Information Science” vs. “Computer Science”) • Early IR work was mostly done in the context of library and information science (LIS) • I-School initiative/movement: drop “library” and enlarge the scope to “informatics”, leading to merger of CS + LIS • Another example where the boundary between fields is disappearing (setting boundaries is generally harmful for research, but is sometimes needed in practice)

  8. IR and Software Engineering • Scalability of IR wasn’t a major concern until the Web • Data collection was relatively small and didn’t grow quickly until the Web • The most effective retrieval models remain simple models based on bag-of-words representation • However, scalability has always been a core issue in IR, and how to engineer an IR system optimally is extremely important for IR applications • Nowadays, data-intensive computing is essential for large-scale IR applications

  9. IR and Applications • Early days: library search, literature • 1970s: small-scale online search systems • 1990s: large-scale systems • TREC (mostly news data, later other kinds of data) • Web search engines • 2010s: search is everywhere! • More and more applications in the future

  10. Publications/Societies (broad view) Learning/Mining Applications ICML ISMB WWW ICML, NIPS, UAI WSDM RECOMB, PSB ACM SIGKDD Info. Science ICDM, SDM Info Retrieval JASIS Statistics JCDL ACM SIGIR AAAI HLT Databases ECIR, CIKM, TREC TOIS, IRJ, IPM NLP ACL ACM SIGMOD,VLDB COLING, EMNLP, NAACL OSDI ICDE, EDBT, TODS Software/systems

  11. Major IR Publication Venues 2010 <1960 1990 1970 1980 2000 ACM SIGIR 1978 CIKM 1994 ECIR 1978 WWW 1994 WSDM 2008 TREC 1992 ACM TOIS 1983 IMP(ISR) 1965 IRJ 1998 JASIST 1950 JDoc 1945

  12. IR Research Topics (Broad View) Users Retrieval Applications Summarization Visualization Analytics Applications Filtering Mining Information Organization Information Access Text Mining Search Extraction Categorization Clustering Natural Language Content Analysis Text Text Acquisition

  13. IR Topics (narrow view) docs 4. Efficiency & scalability INDEXING Query Rep query 3. Document representation/structure Doc Rep 6. User interface (browsing) User Ranking SEARCHING 1. Evaluation 2. Retrieval (Ranking) Models results 5. Search result summarization/presentation INTERFACE Feedback judgments 7. Feedback/Learning QUERY MODIFICATION LEARNING Topics covered most in this course: 2, 3, 5, 7

  14. Major Research Milestones Indexing: auto vs. manual • Early days (late 1950s to 1960s): foundation and founding of the field • Luhn’s work on automatic encoding • Cleverdon’s Cranfield evaluation methodology and index experiments • Salton’s early work on SMART system and experiments • 1970s-1980s: a large number of retrieval models • Vector space model • Probabilistic models • 1990s: further development of retrieval models and new tasks • Language models • TREC evaluation • 2000s-present: more applications, especially Web search and interactions with other fields • Web search • Learning to rank • Scalability (e.g., MapReduce) Evaluation System Indexing + Search Theory Large-scale evaluation, beyond ad hoc retrieval Web search Machine learning Scalability

  15. Frontier Topics in IR: Overview • Two types of topics • 30%: Fundamental challenges: IR models, evaluation, efficiency, user models/studies • 70%: Application-driven challenges: Web (1.0, 2.0, 3.0?), Enterprise (text analytics), Scientific Research (bioinformatics, …) • Methodology • 50%: Machine learning (feature set + supervised) • 30%: Language models (unigram + unsupervised) • 20%: Others (user studies, empirical experiments) • Trends • More interdisciplinary and internationalized • More diversification of topics (new applications, new methods) • Hard fundamental problems regularly revisited

  16. Topics in SIGIR 2011/2012 CFP • Document Representation and Content Analysis (e.g., text representation, document structure, linguistic analysis, non-English IR, cross-lingual IR, information extraction, sentiment analysis, clustering, classification, topic models, facets) • Queries and Query Analysis (e.g., query representation, query intent, query log analysis, question answering, query suggestion, query reformulation) • Users and Interactive IR (e.g., user models, user studies, user feedback, search interface, summarization, task models, personalized search) • Retrieval Models and Ranking (e.g., IR theory, language models, probabilistic retrieval models, feature-based models, learning to rank, combining searches, diversity) • Search Engine Architectures and Scalability ( e.g., indexing, compression, MapReduce, distributed IR, P2P IR, mobile devices) • Filtering and Recommending (e.g., content-based filtering, collaborative filtering, recommender systems, profiles) • Evaluation (e.g., test collections, effectiveness measures, experimental design) • Web IR and Social Media Search (e.g., link analysis, query logs, social tagging, social network analysis, advertising and search, blog search, forum search, CQA, adversarial IR, vertical and local search) • IR and Structured Data (e.g., XML search, ranking in databases, desktop search, entity search) • Multimedia IR (e.g., Image search, video search, speech/audio search, music IR) • Other Applications (e.g., digital libraries, enterprise search, genomics IR, legal IR, patent search, text reuse)

  17. My View of the Future of IR Full-Fledged Text Info. Management Task Support Mining Access Personalization (User Modeling) Search History Entities-Relations Large-Scale Semantic Analysis Complete User Model Knowledge Representation Search Current Search Engine Keyword Queries Bag of words

  18. What You Should Know • IR is a highly interdisciplinary area interacting with many other areas, especially NLP, ML, DB, HCI, software systems, and Information Science • Major publication venues, especially ACM SIGIR, ACM CIKM, ACM TOIS, IRJ, IPM, WSDM

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