This data set consists of (a) 129,000 abstracts describing NSF awards for basic research, (b) bag-of-word data files extracted from the abstracts, (c) a list of words for indexing the bag-of-word data.
Abstracts provided by Michael J. Pazzani ICS Department, School of Computer Science, UCI, Irvine CA, 92697, USA firstname.lastname@example.org Bag-of-word data provided by Amnon Meyers ICS Department, School of Computer Science, UCI, Irvine CA, 92697, USA email@example.comDate Donated: November 18, 2003
The abstracts, one per file, were furnished by the NSF (National Science Foundation). A sample abstract is shown in the next section.
The bag-of-word data was produced by automatically processing the abstracts with a text analyzer called NSFAbst, built using VisualText. While most fields of the output are very accurate, the authors were not extracted from the Investigator: field with 100% accuracy, due to wide variability in that field.
The word list came from a separate process, and may not include all the words of interest in the abstracts.
================================================================== Title : CAREER: Markov Chain Monte Carlo Methods Type: Award NSF Org : CCR Latest Amendment Date : May 5, 2003 File : a0237834 Award Number: 0237834 Award Instr.: Continuing grant Prgm Manager: Ding-Zhu Du CCR DIV OF COMPUTER-COMMUNICATIONS RESEARCH CSE DIRECT FOR COMPUTER & INFO SCIE & ENGINR Start Date : August 1, 2003 Expires : May 31, 2008 (Estimated) Expected Total Amt. : $400000 (Estimated) Investigator: Eric Vigoda firstname.lastname@example.org (Principal Investigator current) Sponsor : University of Chicago 5801 South Ellis Avenue Chicago, IL 606371404 773/702-8602 NSF Program : 2860 THEORY OF COMPUTING Fld Applictn: Program Ref : 1045,1187,9216,HPCC, Abstract : Markov chain Monte Carlo (MCMC) methods are an important algorithmic device in a variety of fields. This project studies techniques for rigorous analysis of the convergence properties of Markov chains. The emphasis is on refining probabilistic, analytic and combinatorial tools (such as coupling, log-Sobolev, and canonical paths) to improve existing algorithms and develop efficient algorithms for important open problems. Problems arising in computer science, discrete mathematics, and physics are of particular interest, e.g., generating random colorings and independent sets of bounded-degree graphs, approximating the permanent, estimating the volume of a convex body, and sampling contingency tables. The project also studies inherent connections between phase transitions in statistical physics models and convergence properties of associated Markov chains. The investigator is developing a new graduate course on MCMC methods. ==================================================================
idnsfid.txt = docid NSF_doc_id (e.g., 1 a9000006) docauths.txt = docid Author_string (e.g., 7 Brian Fiedler) doctitles.txt = docid Title_string (e.g., 9 Ship Operations) docwords.txt = docid wordid freq (e.g., 1 9792 1) Definitions: docid = a counter generated for each document as it was processed. wordid = the id for a word, as obtained from the word.txt file. freq = the number of times that the word (wordid) appears in the file (docid). NSF_doc_id = the value taken from the File: field of an NSF awards file. Title_String = the value of the Title: field of an NSF awards file. Author_String = derived from the Investigator: field when feasible.