2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Prepare Yourself for IR Research ChengXiang Zhai Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, What It Takes to Do Research Curiosity: allow you to ask questions Critical thinking: allow you to challenge assumptions Learning: take you to the frontier of knowledge Persistence: so that you don’t give up Respect data and truth: ensure your research is solid Communication: allow you to publish your work …

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Learning about IR Start with an IR text book (e.g., Manning et al., Grossman & Frieder, a forth-coming book from UMass,…) Then read “Readings in IR” by Karen Sparck Jones, Peter Willett And read papers recommended in the following article: moffat.pdf moffat.pdf Read other papers published in recent IR/IR-related conferences

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Learning about IR (cont.) Getting more focused –Choose your favorite sub-area (e.g., retrieval models) –Extend your knowledge about related topics (e.g., machine learning, statistical modeling, optimization) Stay in frontier: –Keep monitoring literature in both IR and related areas Broaden your view: Keep an eye on –Industry activities Read about industry trends Try out novel prototype systems –Funding trends Read request for proposals

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Critical Thinking Develop a habit of asking questions, especially why questions Always try to make sense of what you have read/heard; don’t let any question pass by Get used to challenging everything Practical advice –Question every claim made in a paper or a talk (can you argue the other way?) –Try to write two opposite reviews of a paper (one mainly to argue for accepting the paper and the other for rejecting it) –Force yourself to challenge one point in every talk that you attend and raise a question

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Respect Data and Truth Be honest with the experiment results –Don’t throw away negative results! –Try to learn from negative results Don’t twist data to fit your hypothesis; instead, let the hypothesis choose data Be objective in data analysis and interpretation; don’t mislead readers Aim at understanding/explanation instead of just good results Be careful not to over-generalize (for both good and bad results); you may be far from the truth

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Communications General communications skills: –Oral and written –Formal and informal –Talk to people with different level of backgrounds Be clear, concise, accurate, and adaptive (elaborate with examples, summarize by abstraction) English proficiency Get used to talking to people from different fields

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Persistence Work only on topics that you are passionate about Work only on hypotheses that you believe in Don’t draw conclusions prematurely –positive results may be hidden in negative results –In many cases, negative results don’t completely reject a hypothesis Be comfortable with criticisms about your work (learn from negative reviews of a rejected paper) Think of possibilities of repositioning the work

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Optimize Your Training Know your strengths and weaknesses –strong in math vs. strong in system development –creative vs. thorough –… Train yourself to fix weaknesses Find strategic partners Position yourself to take advantage of your strengths

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, “Short-Cut” for starting IR research Scan most recently published papers to find papers that you like or can understand Read such paper in detail Track down background papers to increase your understanding Brainstorm ideas of extending the work –Start with ideas mentioned in the future work part –Systematically question the solidness of the paper (have the authors answered all the questions? Can you think of questions that aren’t answered?) –Is there a better formulation of the problem –Is there a better method for solving the problem –Is the evaluation solid? Pick one new idea and work on it

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Some Possible Entry Points NLP-perspective: study of indexing units, word sense disambiguation, weighting based on linguistic knowledge, sentiment retrieval,… ML-perspective: many possible entry points (e.g., learning to rank, text categorization) DM-perspective: indexing with frequent patterns, text OLAP,… DB-perspective: many (e.g., query language, query optimization, indexing structures), especially integration of text and relational data, XML Network-perspective: P2P search, distributed IR, … HCI-perspective: interface for interactive search, user studies, … Others: special applications of IR …

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Next Lecture: Find a good IR research topic