group-, specialized computer ai& that are designed for use by colIab
orative work groups, has produced measurable productivity gains
for major corporations in recent years. Software for electronic meetings
in particular, can help reduce the time required for managers to
complete complex projects hy 90%, according to a recent article in
Fortune. Electronic meeting software helps improve meeting quality
by permitting anonymous comments over networked computers and by
encouraging equal membership participation during a meeting.
It does not use up-to-date technology (e.g. hop field net). But is seems to be the first attempt to solve large-scale brainstorming/clustering problems with A.I. techniques.
For a reason I don't know, they stopped working on this topic.
%0 Journal Article
%1 citeulike:248984
%A Chen, Hsinchun
%A Hsu, P.
%A Orwig, Richard E.
%A Hoopes, L.
%A Nunamaker, Jay F.
%D 1994
%J Commun. ACM
%K classification concept text
%N 10
%P 56--73
%T Automatic Concept Classification of Text from Electronic Meetings
%V 37
%X group-, specialized computer ai& that are designed for use by colIab
orative work groups, has produced measurable productivity gains
for major corporations in recent years. Software for electronic meetings
in particular, can help reduce the time required for managers to
complete complex projects hy 90%, according to a recent article in
Fortune. Electronic meeting software helps improve meeting quality
by permitting anonymous comments over networked computers and by
encouraging equal membership participation during a meeting.
@article{citeulike:248984,
abstract = {group-, specialized computer ai\& that are designed for use by colIab
orative work groups, has produced measurable productivity gains
for major corporations in recent years. Software for electronic meetings
in particular, can help reduce the time required for managers to
complete complex projects hy 90%, according to a recent article in
Fortune. Electronic meeting software helps improve meeting quality
by permitting anonymous comments over networked computers and by
encouraging equal membership participation during a meeting.},
added-at = {2007-08-20T20:41:00.000+0200},
author = {Chen, Hsinchun and Hsu, P. and Orwig, Richard E. and Hoopes, L. and Nunamaker, Jay F.},
biburl = {https://www.bibsonomy.org/bibtex/2b0bf0523d6c49a9f5bcf7ac80e9de644/wnpxrz},
citeulike-article-id = {248984},
comment = {It does not use up-to-date technology (e.g. hop field net). But is seems to be the first attempt to solve large-scale brainstorming/clustering problems with A.I. techniques.
For a reason I don't know, they stopped working on this topic.},
interhash = {cc6bd54fea35a5e3a8c8850e7574850e},
intrahash = {b0bf0523d6c49a9f5bcf7ac80e9de644},
journal = {Commun. ACM},
keywords = {classification concept text},
number = 10,
pages = {56--73},
priority = {0},
timestamp = {2007-08-20T20:41:00.000+0200},
title = {Automatic Concept Classification of Text from Electronic Meetings},
volume = 37,
year = 1994
}