Hi all,
Thanks a lot for your comments. I have just updated my proposal. Looking
forward to your further feedback.
Best Regards,
Wei
On Wed, May 1, 2013 at 5:59 PM, Joachim Daiber <[email protected]>wrote:
> I was referring to the layout of the formulas. Here is a screenshot.
>
> Jo
>
>
> On Wed, May 1, 2013 at 11:14 AM, Wang Wei <[email protected]> wrote:
>
>> Hi Jo,
>>
>> Thanks for your comments.
>>
>>
>>
>> - there is a formating problem for me starting with your github
>> account
>>
>> what do you mean by formating problem?
>>
>>
>> - could you fix the formulas? do you mean the formulas in my
>> proposal?
>>
>>
>>
>> I revised the paragraph for introducing the three models. (Still not good
>> enough..., I'll try to improve it later)
>>
>>
>>
>>
>> On Wed, May 1, 2013 at 2:37 PM, Wang Wei <[email protected]> wrote:
>>
>>> Hi Jo,
>>>
>>> I revised my proposal by adding the topic-based disambiguation method.
>>> Actually, I found a mistake in my previous discussion on the topic-based
>>> model. The topic-based model jointly models *the context
>>> compatibility(which I missed)* and topic coherence. Thus, it can
>>> perform well for both topic related and non-related entities. It is also a
>>> generative model.
>>>
>>> Thanks.
>>>
>>> Regards,
>>> Wei
>>>
>>>
>>> On Mon, Apr 29, 2013 at 10:03 PM, Wang Wei <[email protected]> wrote:
>>>
>>>> Hi Jo,
>>>>
>>>> I will think about the disambiguation part and revise my proposal.
>>>> Thanks.
>>>>
>>>> Best Regards,
>>>> Wei Wang
>>>>
>>>>
>>>> On Mon, Apr 29, 2013 at 7:57 PM, Wang Wei <[email protected]> wrote:
>>>>
>>>>> 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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