Hi Jo,
I read the proposal of the topic extraction work from last year's GSoC.
Yes, the topic-based disambiguation method is based on the LDA model. But
their objectives are different: topic extraction is to assign topic
categories for a document, while topic-based disambiguation is to
disambiguate entities based on the document's topic. For example, if a
document's topic is about 'mobile phones', then word 'Apple' would likely
be assigned as Apple Inc. . But, as I mentioned in my proposal, for topic
related entities, they can be disambiguated correctly; for other entities,
they are not guaranteed to be disambiguated correctly by topic-based
disambiguation method.
In addition, for the generative model(the default disambiguation model), it
has a strong assumption: p(c|e)=p_e(t_1) p_e(t_2)...p_e(t_n), i.e., the
terms are independent given the entity e. Some improvements may be achieved
if this assumption is removed.
Thanks.
Best Regards,
Wei Wang
On Mon, Apr 22, 2013 at 2:30 AM, Joachim Daiber <[email protected]>wrote:
> Hey,
>
> you can have a look at Hector's github repository from last GSoC (this is
> not merged into the main branch yet):
>
> https://github.com/hunterhector/dbpedia-spotlight
>
> I think this was the paper he implemented:
>
> Han, X., 2011. Collective Entity Linking in Web Text : A Graph-Based
> Method. In *Proceedings of the 34th international ACM SIGIR conference on
> Research and development in Information*. pp. 765-774.
>
> Best,
> Jo
>
>
>
>
>
> On Sun, Apr 21, 2013 at 6:22 PM, Wang Wei <[email protected]> wrote:
>
>> Hi Jo,
>>
>> I am trying to learning something about the idea for "efficient graph
>> based disambiguation". However, the introduction is very short. Do you have
>> any further materials for the disambiguation methods used by db-pedia? In
>> the disambiguation code directory[1], which one is graph based?
>>
>> Thanks a lot.
>>
>>
>> Best Regards,
>> Wei Wang
>>
>>
>>
>
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