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<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>Bob Parks wrote:<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>“</span></font>I'm interested in
finding out which words are the most central nodes <br>
in a dictionary…<font size=2 color=blue face=Arial><span
style='font-size:10.0pt;font-family:Arial;color:blue'>”<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>I would be very interested in the results
of your research. I can imagine a way to produce a simple database that could
be mined for central nodes. For instance I know of software that could sort a
database by the frequency of a “node”. If you had an electronic
copy of Wordsmyth, it would probably take a day or so to generate the database.
Essentially you would have to break each definition into separate fields, one
word per field. Then you would reverse the database to produce a list of the
defining vocabulary. Then you would sort the database by frequency—yielding
a list of words used in definitions with the most frequent ones first. We could
keep the original headwords and definitions so that you could look for multiple
senses of the defining words (e.g. ‘a *<b><span style='font-weight:bold'>kind</span></b>*
of shoe’ versus ‘to be *<b><span style='font-weight:bold'>kind</span></b>*
and compassionate’). We could do the same thing with synonyms and
antonyms (and any other lexical relations in the database). I’ve done
this sort of thing before. None of it is particularly hard.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>I just read Steyvers and Tenenbaum. I don’t
know anything about statistics, so I didn’t gain much from it. But the
article raises a number of issues that I am very interested in. Their semantic
networks and central nodes are very similar to semantic domains and other
perspectives on semantics. I’ve been trying to get a handle on why the
mental lexicon tends to clump and cluster around key words.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'>Ron Moe<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 color=blue face=Arial><span style='font-size:
10.0pt;font-family:Arial;color:blue'><o:p> </o:p></span></font></p>
<div>
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face="Times New Roman"><span style='font-size:12.0pt'>
<hr size=2 width="100%" align=center tabindex=-1>
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<p class=MsoNormal><b><font size=2 face=Tahoma><span style='font-size:10.0pt;
font-family:Tahoma;font-weight:bold'>From:</span></font></b><font size=2
face=Tahoma><span style='font-size:10.0pt;font-family:Tahoma'>
lexicographylist@yahoogroups.com [mailto:lexicographylist@yahoogroups.com] <b><span
style='font-weight:bold'>On Behalf Of </span></b>Bob Parks<br>
<b><span style='font-weight:bold'>Sent:</span></b> Friday, November 14, 2008
12:52 PM<br>
<b><span style='font-weight:bold'>To:</span></b>
lexicographylist@yahoogroups.com<br>
<b><span style='font-weight:bold'>Subject:</span></b> Re: [Lexicog] Digest
Number 1050</span></font><o:p></o:p></p>
</div>
<p class=MsoNormal><font size=3 face="Times New Roman"><span style='font-size:
12.0pt'><o:p> </o:p></span></font></p>
<div id=ygrp-mlmsg>
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<p style='margin-bottom:12.0pt'><font size=3 face="Times New Roman"><span
style='font-size:12.0pt'>Greetings:<br>
I'm interested in finding out which words are the most central nodes <br>
in a dictionary - i.e., the words that are most used in definitions <br>
(aside from stop words); as well as those that have the largest <br>
number of synonyms/antonyms, etc. The analysis in Steyvers and <br>
Tennenbaum, "The Large-Scale Structure of Semantic Networks" seems to
<br>
indicate there is a "small worlds" network in WordNet, but they don't
<br>
give an actual list of the central node-words. I'm interested in <br>
doing this sort of an analysis with the Wordsmyth <br>
Dictionary-Thesauru<wbr>s. Does anyone have any suggestions for analysis? <br>
Would Ken Litkowski's "Di-graph" software discover this sort of a <br>
network? Any advice/assistance is appreciated.<br>
Bob Parks<br>
-- <br>
* The best dictionary and integrated thesaurus on the web: <br>
<a href="http://www.wordsmyth.net">http://www.wordsmyt<wbr>h.net</a><br>
* Robert Parks - Wordsmyth - (607) 272-2190<br>
* "To imagine a language is to imagine a form of life." (LW)<br>
* "Philosophers have only interpreted the world. The point, however, <br>
is to change it." (KM)<br>
* Community grows as we communicate, honing our words till their <br>
meanings tap the rich voice of our full human potential.<o:p></o:p></span></font></p>
</div>
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