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    <Metadata name="Title">Bronwyn; page: 1 of 1 1 Using language models for generic entity extraction</Metadata>
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    <Metadata name="dc.Creator">Ian H. Witten</Metadata>
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    <Metadata name="dc.Creator">W.J. Teahan</Metadata>
    <Metadata name="dc.Title">Using language models for generic entity extraction</Metadata>
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  <Content>&lt;pre&gt;
Bronwyn; page: 1 of 1 1 Using language models for generic entity extraction
Ian H. Witten, Zane Bray, Malika Mahoui, W.J. Teahan Computer ScienceUniversity
of WaikatoHamilton, New [email protected] AbstractThis paper describes
the use of statisticallanguage modeling techniques, such as arecommonly used
for text compression, to extractmeaningful, low-level, information about
thelocation of semantic tokens, or \\322entities,\\323 in text.We begin by
marking up several different tokentypes in training documents\\321for example,people\\325s
names, dates and time periods, phonenumbers, and sums of money. We form alanguage
model for each token type and examinehow accurately it identifies new tokens.
We thenapply a search algorithm to insert tokenboundaries in a way that maximizes
compressionof the entire test document. The technique can beapplied to hierarchically-defined
tokens, leadingto a kind of \\322soft parsing\\323 that will, we believe,be
able to identify structured items such asreferences and tables in html or
plain text, basedon nothing more than a few marked-up examplesin training
documents. 1.   INTRODUCTIONText mining is about looking for patterns in
text, and maybe defined as the process of analyzing text to extractinformation
that is useful for particular purposes.Compared with the kind of data stored
in databases, textis unstructured, amorphous, and difficult to deal with.Nevertheless,
in modern Western culture, text is the mostcommon vehicle for the formal
exchange of information.The motivation for trying to extract information
from it iscompelling\\321even if success is only partial.Text mining is possible
because you do not have tounderstand text in order to extract useful information
fromit. Here are four examples. First, if only names could beidentified,
links could be inserted automatically to otherplaces that mention the same
name\\321links that are\\322dynamically evaluated\\323 by calling upon a search
engineto bind them at click time. Second, actions can beassociated with different
types of data, using eitherexplicit programming or programming-by-demonstrationtechniques.
A day/time specification appearing anywherewithin one\\325s email could be
associated with diary actionssuch as updating a personal organizer or creating
anautomatic reminder, and each mention of a day/time in thetext could raise
a popup menu of calendar-based actions.Third, text could be mined for data
in tabular format,allowing databases to be created from formatted tablessuch
as stock-market information on Web pages. Fourth,an agent could monitor incoming
newswire stories forcompany names and collect documents that mentionthem\\321an
automated press clipping service.In all these examples, the key problem is
to recognizedifferent types of target fragments, which we will calltokens
or \\322entities\\323. This is really a kind of languagerecognition problem:
we have a text made up of differentsublanguages {for personal names, company
names, dates,table entries, and so on} and seek to determine whichparts are
expressed in which language.The information extraction research community
{of whichwe were, until recently, unaware} has studied these tasksand reported
results at annual Message UnderstandingConferences {MUC}. For example, \\322named
entities\\323 aredefined as proper names and quantities of interest,including
personal, organization, and location names, aswell as dates, times, percentages,
and monetary amounts{Chinchor, 1999}.The standard approach to this problem
is manual:tokenizers and grammars are hand-designed for theparticular data
being extracted. Looking at currentcommercial state-of-the-art text mining
software, forexample, IBM\\325s Intelligent Miner for Text {Tkach, 1997}uses
specific recognition modules carefully programmedfor the different data types,
while Apple\\325s data detectors{Nardi et al., 1998} uses language grammars.
The  TextTokenization Tool of Grover et al. {1999} is anotherexample, and
a demonstration version is available on theWeb. The challenge for machine
learning is to use
&lt;/pre&gt;</Content>
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