Well, we can definitely see that en-eo has a bad time with translating
Turkish: 

@*berryhuckle *yok *ben *sana *faceten ĉeıyıım *sen *yine *çok *güzel
*çıkm.ı*şsın *ben *yine *kötü *çıkm.ı*şım *ama *olsun *fotoğ*rafımızın
*olması *iyi *bişeyy :))

:)

F.

El dl 17 de 03 de 2014 a les 14:18 -0400, en/na Saurabh Hota va
escriure:
> Hi
> I have gone through the archives and Akshay has good data set
> of shortened words which can be used to train which vowels are
> dropped. Also we have to note that abbreviations and shorten form
> are different like brb - > be right back and bday -> birthday. So we
> have to handle them separately. And to do this first we have to
> classify them.
> 
> 
> For translation I have just written a bash script
>     while read line; do echo $line | apertium en-eo; done < Tweets
> 
> 
> Tweets and their translation.
> 
> 
> On Mon, Mar 17, 2014 at 6:15 AM, Francis Tyers <fty...@prompsit.com>
> wrote:
>         El dl 17 de 03 de 2014 a les 13:46 +0530, en/na Saurabh Hota
>         va
>         escriure:
>         > Hi Francis
>         >
>         >
>         > I am Saurabh, a fourth year undergraduate student majoring
>         in Computer
>         > Science
>         > at Indian Institute of Technology. I am interested to work
>         on
>         > improving support
>         > for non standard words(NSW).
>         >
>         >
>         > I have read some papers and have a vast collection of
>         general tweets,
>         > form that
>         > I have observed that classification of non standard words is
>         > important. Like the
>         > top level classification could be numerical and alphabetical
>         NSW and
>         > they can
>         > be classified further also and then handle them separately.
>         >
>         >
>         > Below I am listing how to handle some of them (easy to
>         hard):
>         > 1. Emoticons can be handled easily as they are very limited.
>         > 2. Repeating letter in a word (eg byeee etc) can be
>         normalized by
>         > reducing letter
>         >     which occur more than 3 times to 1 or 2 times and
>         checking it
>         > whether it is
>         >     present in the dictionary
>         > 3. Shortened words are difficult to handle. eg insti ->
>         institution,
>         > ur -> your etc.
>         >     We can handle this if we assume that corpus also contain
>         the exact
>         > standard
>         >     word of the shortened word. eg one sentence is
>         >          Institute building is far away.
>         >     and another sentence is
>         >          Today Insti building is been renovated
>         >     So from this we know that Insti might be shortened form
>         of
>         > Institute with some
>         >     probability which can calculated assuming n-gram model.
>         This is an
>         > unsupervised
>         >     method.
>         >
>         >
>         >     Another way to handle this if we can get a training set
>         containing
>         > exact words
>         >     their shortened form then we can train (training method
>         can be
>         > decided later but
>         >     an easy choice can be naive bayes) which letters are
>         generally
>         > dropped.
>         >     Then for each word we will can find their probable
>         shortened form.
>         >
>         >
>         > The are many other types of NSW but now I am focused on
>         above ones.
>         > So, Sir can you review these ideas and give some
>         suggestions.
>         >
>         
>         
>         Hello, that sounds quite good! I recommend you take a look at
>         the
>         mailing list archive to see what Karan and Akshay have come up
>         with, and
>         come back to us with what you think.
>         
>         Also, which language did you translate the tweets to with
>         Apertium ?
>         
>         F.
>         
>         F.
>         
>         
>         
>         
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