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

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

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 Computer Vision Information Retrieval Databases Natural Language Processing Software engineering Computer systems Algorithms Systems

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

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)

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

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)

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

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

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

Major IR Publication Venues 2010 <1960 1970 1980 1990 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

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

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

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

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

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)

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

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