happy new year to all of you!

I forgot to follow up on this topic of sentence boundaries in srilm and 
moses. maybe I missed the answer - but I don't recall that someone 
answered the discussion below.

how does moses do it? adding sentence boundaries or not? or does srilm 
always assume that a string is a full sentence when called for computing 
the LM score? what are the consequences for the incremental decoding 
procedure in that case? and if sentence boundaries are added in the 
internal calls to srilm - what happens when moses uses irstlm instead?

could someone clarify? thanks in advance!

jorg


> 
> El vie, 14-11-2008 a las 08:21 +0100, Marcello Federico escribió:
>> Felipe,
>>
>> correct, irstlm does not add sentence boundaries.
>> irstlm uses them only if you add them to the data.
>>
>> srilm adds sentence boundaries by default around each 
>> text line but you can disable this operation (check proper
>> option in the manual page of ngram-count and ngram).
>>
>> i'm not sure about how moses calls srilm internally.
>> my guess is that only single n-grams are passes to
>> srilm and that no sentence boundary symbols are
>> introduced by moses.
>>
>> marcello
>>
>> ________________________________________
>> From: [email protected] [[email protected]] On 
>> Behalf Of J.Tiedemann [[email protected]]
>> Sent: Thursday, November 13, 2008 11:06 PM
>> To: [email protected]; [email protected]
>> Subject: Re: [Moses-support] Translating words or phrases in isolation
>>
>> I'm not 100% sure but I think that IRSTLM does not add sentence
>> boundary tokens. maybe that's an option?
>>
>> jorg
>>
>>
>> On Thu, 13 Nov 2008 20:58:54 +0100
>>   Felipe Sánchez Martínez <[email protected]> wrote:
>>> Hi all,
>>>
>>> I am using Moses to obtain translation candidates (in the form of
>>> n-best
>>> lists) for phrases or words in isolation; that is, I am not
>>> translating
>>> whole (well-formed) sentences.
>>>
>>> Does SRILM (the language model I am using with Moses) introduce a
>>> begin-of-sentence token before computing the likelihood of the input
>>> sentence (in my case a phrase or a word).
>>>
>>> If the question to the previous question is yes. How could I avoid
>>> that?
>>>
>>> Thank you very much in advance,
>>>
>>> Kind regards
>>>
>>> --
>>> Felipe
>>>
>>> _______________________________________________
>>> Moses-support mailing list
>>> [email protected]
>>> http://mailman.mit.edu/mailman/listinfo/moses-support
>> _______________________________________________
>> Moses-support mailing list
>> [email protected]
>> http://mailman.mit.edu/mailman/listinfo/moses-support
> 
> _______________________________________________
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> [email protected]
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-- 

Jörg


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