Encouraging words regarding applications of word sense disambiguation.
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---------- Forwarded message ----------
Date: Thu, 19 Aug 2004 14:10:01 -0400
From: "Amsler, Robert" <[EMAIL PROTECTED]>
To: [EMAIL PROTECTED]
Subject: RE: [Senseval-discuss] more on applications of wsd

I feel I should comment on this discussion of WSD since I am now involved
almost completely in a significant application of such technology. At the
Dept. of Energy I work on detecting what we'll call "sensitive information"
in documents such that human reviewers can then decide whether the documents
have to be withheld from release. The task is difficult because the volume
and variety of electronic documents awaiting examination appears to be
growing geometrically while the amount of sensitive information in the
documents is actually not increasing. It is a typical "needle in a haystack"
task of identifying a very small amount of sensitive content in a very large
amount of irrelevant text.

The task is performed by essentially searching for specific words and
phrases whose likelihood of being related to the sensitive content is quite
high, but there are many specific words which cannot readily be immediately
searched for because without word sense disambiguation they return false
positives on the order of 100:1 or worse. (From experience it is clear that
when human readers are given very high percentages of false positives they
begin to distrust the tool that provides the data and eventually will reject
the term and the tool as not worth their time since it cannot discriminate
between vitally important and totally irrelevant senses of a word or
phrase.)

While it may seem that WSD cannot achieve success until an overall score of
90% or above for general vocabulary can be realized, I do not find that a
very interesting goal. I am more concerned with specific words that have
extraordinarily low likelihoods of having the one precise meaning one needs
to find amidst an overwhelming predominance of irrelevant meanings. It does
little good in my application to speak in averages across the whole
vocabulary. What would be far more useful would be explicit treatments of
some highly ambiguous word or phrase from which definitive techniques for
their separation might lead to the creation of general methods of attack on
other specific words and phrases. I'd prefer someone to claim they had
achieved 99% accuracy in distinguishing senses of some term such as "line"
than in claiming they had reached 90% across the general vocabulary (with
perhaps only 40% on "line" itself, but 99% on other words).

I believe that WSD may achieve break-out success not from its "overall"
ability, but from the ability to craft tools for specific applications to
narrow vocabularies. Think of the situation with weather forecasts and MT in
which being able provide French/English translations in a small domain
became a successful application while general MT labors on.

For an example, consider the task of distinguishing the terms for chemical
elements from other words and abbreviations in text. I, Be, He, In, At and
As are chemical elements, but overwhelmingly more common as English function
words--how would one reliably find their chemical element senses? Likewise,
Ca, Mn, Co, Ga, Al, Ne, Ar, In, La, Nd, Pa, and Nd are both chemical
elements and 2-letter abbreviations for US states. Separating the two
classes would be a good challenge. And the preferred solution should not
depend on upper/lower case information since that is sometimes unavailable
or unreliable.

In general, if you talk to people doing my type of work, what they generally
admit is that short acronyms are nearly impossible to disambiguate in text.
They vitally need to know when something like DNA refers to the Defense
Nuclear Agency, but can't afford to search for it because the now
overwhelming presence of the genetic meaning will overwhelm the sense they
want. Build an acronym disambiguator and the world will beat a path to your
door.


Robert Amsler


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