kdd-tutorial.ppt

Andrew McCallum, Just Research Information Extractionand Integration: an Overview William W. Cohen Carnegie Mellon University Jan 12, 2004 Administrivia Course web page: /~wcohen/10-707/ (or Google me) No class 1/28 or 2/2. Unless I get a volunteer? Classwork: Write ? page on each paper being discussed. Starting next week. Present 1 or 2 “optional” papers. Do a course project. Today’s lecture Overview of the task of information extraction. Overview of some methods for “named entity extraction”: Sliding windows, boundary-finding: reduce NE to classification. HMM, CMM, CRF: reduce NE to sequential classification (an independently interesting problem) Overview of some methods for associating, (grouping, clustering, querying, using, …) extracted data. Example: The Problem Example: A Solution Extracting Job Openings from the Web What is “Information Extraction” What is “Information Extraction” What is “Information Extraction” What is “Information Extraction” What is “Information Extraction” What is “Information Extraction” IE in Context Tutorial Outline IE History Landscape of problems and solutions Parade of models for segmenting/classifying: Sliding window Boundary finding Finite state machines Trees Overview of related problems and solutions Association, Clustering Integration with Data Mining Where to go from here IE History Pre-Web Mostly news articles De Jong’s FRUMP [1982] Hand-built system to fill Schank-style “scripts” from news wire Message Understanding Conference (MUC) DARPA [’87-’95], TIPSTER [’92-’96] Early work dominated by hand-built models E.g. SRI’s FASTUS, hand-built FSMs. But by 1990’s, some machine learning: Lehnert, Cardie, Grishman and then HMMs: Elkan [Leek ’97], BBN [Bikel et al ’98] Web AAAI ’94 Spring Symposium on “Software Agents” Much discussion of ML applied to Web. Maes, Mitchell, Etzioni. Tom Mitchell’s WebKB, ‘96 Build KB’s from the Web. Wrapper Induction Initially hand-build, then ML: [Soderland ’96], [Kushmeric ’97],… Citeseer; Co