Textual Data Mining

Sally Jo Cunningham
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:168-174, 1995.

Abstract

Most automated or semi-automated techniques for extracting novel information from data have concentrated on analyzing simple tables of numeric or atomic symbolic values. A related (but much more complex) problem, that of inferring new facts or knowledge from textual databases, has been addressed most effectively by the library and information retrieval research communities. This paper incorporates several ad hoc search strategies proposed by those communities into a single search methodology that guides the search process and provides a framework for the presentation of facts gleaned from the search. This graphical search result representation is semi-formal, in the sense that it represents the structure of search results formally while the contents of the search are represented informally. The methodology is intended as an aid to "mining" new scientific information from textual/bibliographic databases, rather than as an automated proof system.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-cunningham95a, title = {Textual Data Mining}, author = {Cunningham, Sally Jo}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {168--174}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/cunningham95a/cunningham95a.pdf}, url = {https://proceedings.mlr.press/r0/cunningham95a.html}, abstract = {Most automated or semi-automated techniques for extracting novel information from data have concentrated on analyzing simple tables of numeric or atomic symbolic values. A related (but much more complex) problem, that of inferring new facts or knowledge from textual databases, has been addressed most effectively by the library and information retrieval research communities. This paper incorporates several ad hoc search strategies proposed by those communities into a single search methodology that guides the search process and provides a framework for the presentation of facts gleaned from the search. This graphical search result representation is semi-formal, in the sense that it represents the structure of search results formally while the contents of the search are represented informally. The methodology is intended as an aid to "mining" new scientific information from textual/bibliographic databases, rather than as an automated proof system.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Textual Data Mining %A Sally Jo Cunningham %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-cunningham95a %I PMLR %P 168--174 %U https://proceedings.mlr.press/r0/cunningham95a.html %V R0 %X Most automated or semi-automated techniques for extracting novel information from data have concentrated on analyzing simple tables of numeric or atomic symbolic values. A related (but much more complex) problem, that of inferring new facts or knowledge from textual databases, has been addressed most effectively by the library and information retrieval research communities. This paper incorporates several ad hoc search strategies proposed by those communities into a single search methodology that guides the search process and provides a framework for the presentation of facts gleaned from the search. This graphical search result representation is semi-formal, in the sense that it represents the structure of search results formally while the contents of the search are represented informally. The methodology is intended as an aid to "mining" new scientific information from textual/bibliographic databases, rather than as an automated proof system. %Z Reissued by PMLR on 01 May 2022.
APA
Cunningham, S.J.. (1995). Textual Data Mining. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:168-174 Available from https://proceedings.mlr.press/r0/cunningham95a.html. Reissued by PMLR on 01 May 2022.

Related Material