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CONLANG  February 2015, Week 3

CONLANG February 2015, Week 3

Subject:

Re: polysemy/homonymy index (anyone need a good Ph. D. project?)

From:

MorphemeAddict <[log in to unmask]>

Reply-To:

Constructed Languages List <[log in to unmask]>

Date:

Sun, 15 Feb 2015 18:17:26 -0500

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (119 lines)

I wonder if it makes more sense to separate the 33 meanings of the noun
"head" from the 9 meanings of the verb "head", or to consider the 42
meanings of "head", 33 of which are noun and 9 are verb.

I'm sure there are some adjectives meanings in there somewhere, too, e.g.,
"head teacher", which is included in the noun meanings.

stevo

On Sun, Feb 15, 2015 at 7:02 AM, Jyri Lehtinen <[log in to unmask]>
wrote:

> Let's see, a quick search would say that the noun with the most listed
> definitions is "head" and the verb "break". Don't ask what those
> definitions are, the index files only list eight digit definition IDs for
> each word and looking what those mean would require a separate search.
> Anyway, it seems clear that WordNet is fairly fine grained when is comes to
> separating definitions from each other. It's also very liberal in what it
> allows to be counted as separate words. Among nouns given near the top of
> the index.noun file are such things as "15 August 1945" and
> "5-hydroxy-3-methylglutaryl-coenzyme A reductase". Including items like
> this doesn't affect the qualitative results but such issues would need to
> be taken care of in a more serious analysis.
>
>    -Jyri
>
>
> 2015-02-15 8:51 GMT+02:00 MorphemeAddict <[log in to unmask]>:
>
> > Jyri, what were the noun and verb with the most definitions?
> >
> > stevo
> >
> > On Sat, Feb 14, 2015 at 10:17 PM, Jyri Lehtinen <[log in to unmask]
> >
> > wrote:
> >
> > > 2015-02-13 1:51 GMT+02:00 Siva Kalyan <[log in to unmask]
> >:
> > >
> > > > Interesting idea—and it should be easy to do using (English) WordNet.
> > > >
> > > > I suspect polysemy would follow a power law (i.e., lots and lots of
> > words
> > > > with just one sense, very few with lots of senses).
> > > >
> > >
> > > I went and did exactly that. I took the WordNet 3.1 database (
> > > http://wordnet.princeton.edu/wordnet/download/current-version/) and
> > > plotted
> > > separately the distributions of definitions given per word for their
> > lists
> > > of nouns, adjectives, adverbs and verbs. Turns out that the
> distributions
> > > follow indeed power laws. I then went and did quick fits into the
> > > dictionary data to get estimates of the power law exponent (alpha) and
> > back
> > > up the claim of power law behaviour. The results and plots for the
> > > different word classes are:
> > >
> > > Nouns
> > > 117953 words
> > > alpha = -3.81 +- 0.02
> > > http://kirnis.kapsi.fi/kamaa/distr-noun.png
> > >
> > > Adjectives
> > > 21499 words
> > > alpha = -3.40 +- 0.02
> > > http://kirnis.kapsi.fi/kamaa/distr-adjective.png
> > >
> > > Adverbs
> > > 4475 words
> > > alpha = -3.50 +- 0.03
> > > http://kirnis.kapsi.fi/kamaa/distr-adverb.png
> > >
> > > Verbs
> > > 11540 words
> > > alpha = -2.72 +- 0.03
> > > http://kirnis.kapsi.fi/kamaa/distr-verb.png
> > >
> > > The fits are done just by simple curve fitting into the histograms and
> to
> > > me it looks like they underestimate the error bars of the exponents. I
> > did
> > > also try a more refined Bayesian regression but couldn't get the
> sampling
> > > to behave quickly enough for a short play with the data. Still, it
> seems
> > > quite secure to say that the distribution of definitions per word is
> > > flatter (less negative alpha) for verbs than for the other word
> classes,
> > > i.e. verbs have more polysemy. The most definitions given for a noun,
> for
> > > example, is 33 while for verbs with ten times less words in the dataset
> > > it's an incredible 59. I also think that the power law exponent is a
> good
> > > contender for a polysemy index as itself, though you could of course
> also
> > > derive the mean number of definitions per word from it if you so wish.
> > >
> > > This part was quick enough. The hard part, as was already discussed,
> > would
> > > be to gather more dictionary data and ensure that their editing follows
> > > comparable standards. You don't want to be comparing compact travel
> > > dictionaries with thoroughly researched academic ones. It's clear just
> by
> > > glancing that they will show very different levels of polysemy for the
> > same
> > > language. I don't think there would be a full PhD in this but with a
> bit
> > > more time with the statistics and a good discussion of comparing
> > different
> > > datasets you should be able to pull off a good paper.
> > >
> > >    -Jyri
> > >
> >
>

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