Overview of IR Research

ChengXiang Zhai

Department of Computer Science University of Illinois, Urbana-Champaign

What is Information Retrieval (IR)?

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Salton’s definition (Salton 68): “information retrieval is a field concerned with the structure, analysis, organization, storage, searching, and retrieval of information”

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Information: mostly text, but can be anything (e.g., multimedia)

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Retrieval:

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Narrow sense: search/querying

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Broad sense: filtering, classification, summarization, ...

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In more general terms

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Information access

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Information seeking

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Help people manage and make use of all kinds of information

Who are working on IR? (IR and Related Areas)

Applications Models

Statistics Optimization Machine Learning Pattern Recognition Data Mining Natural Language Processing Applications Web, Bioinformatics…

Information Retrieval

Software engineering Computer systems Library & Info Science Databases

Algorithms Systems

IR and NLP

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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

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Most recently the two fields regained close interactions

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More complex retrieval tasks (question answering, opinons)

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More scalable/robust NLP techniques (parsing, extraction)

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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

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“Sibling” fields, but they didn’t get along with each other well

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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

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Major differences in data, user, query, what counts as answers: DB

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efficiency; IR

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effectiveness

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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

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IR as a subfield of AI (IR=intelligent text access)?

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AI is too big to have a coherent community (e.g., ML, NLP, Computer Vision all “spin off”)

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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

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Lack of training data for search (no large-scale online system, users don’t like to make effort on judgments)

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Learning based approach didn’t prevail for ad hoc retrieval

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Machine learning is now very important for IR

IR and Library & Information Science

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Inseparable from day one (“Information Science” vs. “Computer Science”)

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Early IR work was mostly done in the context of library and information science (LIS)

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I School initiative/movement: drop “library” and enlarge the scope to “informatics”, leading to merger of CS + LIS

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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

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Scalability of IR wasn’t a major concern until the Web

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Data collection was relatively small and didn’t grow quickly until the Web

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The most effective retrieval models remain simple models based on bag-of-words representation

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However, scalability has always been a core issue in IR, and how to engineer an IR system optimally is extremely important for IR applications

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Nowadays, data-intensive computing is essential for large-scale IR applications

IR and Applications

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Early days: library search, literature 1970s: small-scale online search systems 1990s: large-scale systems

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TREC (mostly news data, later other kinds of data)

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Web search engines

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2010s: search is everywhere!

More and more applications in the future

Publications/Societies (broad view)

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

Major IR Publication Venues

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

IR Research Topics (Broad View)

Retrieval Applications

Information Access

Users

Summarization Filtering Search

Information Organization

Visualization Mining Extraction Analytics Applications

Text Mining

Categorization Clustering

Natural Language Content Analysis Text

Text Acquisition

IR Topics (narrow view)

docs

SEARCHING

Doc Rep

Query Rep

query 6. User interface (browsing) User 1. Evaluation results Feedback 7. Feedback/Learning

QUERY MODIFICATION LEARNING

“core” topics: 1-4, 7, especially 1, 2, 7

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Major Research Milestones

Early days (late 1950s to 1960s): foundation and founding of the field

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Luhn’s work on automatic encoding Indexing: auto vs. manual

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Cleverdon’s Cranfield evaluation methodology and index experiments Salton’s early work on SMART system and experiments Evaluation System 1970s-1980s: a large number of retrieval models

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Vector space model Probabilistic models Indexing + Search Theory 1990s: further development of retrieval models and new tasks

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Language models Large-scale evaluation, beyond ad hoc retrieval

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TREC evaluation 2000s-present: more applications, especially Web search and interactions with other fields

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Web search Learning to rank Web search Machine learning Scalability

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Scalability (e.g., MapReduce)

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Frontier Topics in IR: Overview

Two types of topics

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30%: Fundamental challenges: IR models, evaluation, efficiency, user models/studies

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70%: Application-driven challenges: Web (1.0, 2.0, 3.0?), Enterprise (text analytics), Scientific Research (bioinformatics, …) Methodology

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50%: Machine learning (feature set + supervised)

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30%: Language models (unigram + unsupervised)

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20%: Others (user studies, empirical experiments) Trends

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More interdisciplinary and internationalized

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More diversification of topics (new applications, new methods)

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Hard fundamental problems regularly revisited

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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) 16

My View of the Future of IR

Task Support

Full-Fledged Text

Mining

Info. Management

Access

Search

Current Search Engine

Keyword Queries Bag of words

Search History

Personalization

Entities-Relations

Large-Scale

Knowledge

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What You Should Know

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IR is a highly interdisciplinary area interacting with many other areas, especially NLP, ML, DB, HCI, software systems, and Information Science

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Major publication venues, especially ACM SIGIR, ACM CIKM, ACM TOIS, IRJ, IPM, WSDM