i implemented this years ago (the idea then was to see if for
free-word-order languages, phrases could be generalised).  at the time
it didn't seem that there was a more efficient way to do it than just
generate permutations and score them.

and if you think about it, this is essentially the reordering problem

Miles

On 25 October 2010 12:59, Philipp Koehn <[email protected]> wrote:
> Hi,
>
> I am not familiar with that, but somewhat related is
> Arne Mauser's global lexical model, which also exists
> as a secret feature in Moses (secret because no
> effiencient training exists):
>
> Citation:
> A. Mauser, S. Hasan, and H. Ney. Extending Statistical Machine
> Translation with Discriminative and Trigger-Based Lexicon Models. In
> Conference on Empirical Methods in Natural Language Processing
> (EMNLP), Singapore, August 2009.
> http://www-i6.informatik.rwth-aachen.de/publications/download/628/MauserArneHasanSav%7Bs%7DaNeyHermann--ExtendingStatisticalMachineTranslationwithDiscriminativeTrigger-BasedLexiconModels--2009.pdf
>
> -phi
>
>
> On Fri, Oct 22, 2010 at 7:02 PM, Francis Tyers <[email protected]> wrote:
>> Hello all,
>>
>> I have a rather strange request. Does anyone know of any papers (or
>> impementations) on bag-of-words language models ? That is, a language
>> model which does not take into account the order in which the words
>> appear in an ngram, so if you have the string 'police chief of' in your
>> model, you will get a result for both 'chief of police' and 'police
>> chief of'. I have thought of using IRSTLM or some generic model and
>> scoring all the permutations, but wondered if there was a more efficient
>> implementation already in existence. I have searched without much luck
>> in Google, but perhaps I am searching with the wrong words.
>>
>> Best regards,
>>
>> Fran
>>
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