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) Information Retrieval Databases Library & Info Science Machine Learning Pattern Recognition Data Mining Natural Language Processing Applications Web, Bioinformatics… Statistics Optimization Software engineering Computer systems Models Algorithms Applications Systems Human-Computer Interaction Computer Vision

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

11. Major IR Publication Venues ACM SIGIR 1990<1960 197020001980 2010 CIKM 1978 ECIR TREC 1978 1994 WWW WSDM 1994 2008 1992 1983 1965 ACM TOIS IMP(ISR) IRJ JASIST JDoc 1998 1950 1945

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

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

14. Major Research Milestones 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) Indexing: auto vs. manual 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 15

16. Topics in SIGIR 2011/2012 CFP 16 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. 17 My View of the Future of IR Bag of words Search Keyword Queries Access Mining Task Support Entities-Relations Knowledge Representation Search History Complete User Model Current Search Engine Personalization (User Modeling) Large-Scale Semantic Analysis Full-Fledged Text Info. Management

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