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Topic: Mining of concurrent text and time series
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Hector Jasso  2
04-29-2001 06:58 PM ET (US)
Edited by author 04-29-2001 06:58 PM
I agree with Greg's comment on the time scale of the
experiments. My concern about that is that if one
does not have an infinite credit (an assumption the
authors make in testing their model), then false
positives and false negatives can have a large impact.
For example, take the case with the Timer Warner/AOL
merge that happened this (last?) year. There was a
lot of talk about whether the merger
would happen. So, this would result in many predictions
of steep increase, because the news were constantly
talking about a possible merge. Let's assume that
as long as the merge is not approved, any sell/buy
action results in random gains, corresponding to
daily fluctuations of the market, with no particular
trend. When the merge is approved, what is the actual
gain? I would say small compared to all the buys
and sells that the system did over the months of
speculation about a possible merge. I can think of
many examples when a decision is "in the air" until
someone confirms it, but the important piece of
information is the confirmation, not the speculation.

But maybe more important than that is whether specific words
represent actual pieces of information: I wonder if
the system can differentiate between a note saying that
the new Pentium chip "has no bugs" and one saying that
it "has bugs." They both have the words "has" and "bugs"!
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