Hi Abhisek,
There is a lot of experimentation which can be done with both 5.16 and
5.17.
In my opinion the current problem is that the Surface Form(SF) matching is
a bit poor.
Mixing the Babelfy Superstring matching with other ideas to make SF
spotting better could be a great start.
You can also bring ideas from papers such as [1] in order to address more
linguistic variations.
It's hard to debate which one is better, however you can mix ideas i.e:
use superstring matching to greedy match more Surface forms with more
linguistic variations, while using word2vec in the disambiguation stage.
Feel free to poke me if you would like to discuss in more detail :)
[1] https://aclweb.org/anthology/P/P11/P11-1095.pdf
On Mon, Mar 2, 2015 at 7:21 PM, Abhishek Gupta <a.gu...@gmail.com> wrote:
> Hi all,
>
> Recently I checked out the ideas list of DBpedia for GSoC 2015 and I
> should admit one thing that every idea is more interesting than the
> previous one. While I was looking out for ideas that interests me I found
> following ideas most fascinating and I wish I could work on all of them but
> unfortunately I couldn't:
>
> 1) 5.1 Fact Extraction from Wikipedia Text
>
> 2) 5.9 Keyword Search on DBpedia Data
>
> 3) 5.16 DBpedia Spotlight - Better Context Vectors
>
> 4) 5.17 DBpedia Spotlight - Better Surface form Matching
>
> 5) 5.19 DBpedia Spotlight - Confidence / Relevance Scores
>
> But in all these I found a couple of ideas interlinked, in other words one
> solution might leads to another. Like in 5.1, 5.16, 5.17 our primary
> problems are Entity Linking (EL) and Word Sense Disambiguation (WSD) from
> raw text to DBpedia entities so as to understand raw text and disambiguate
> senses or entities. So if we can address these two tasks efficiently then
> we can solve problems associated with these three ideas.
>
> Following are some methods which were there in the research papers
> mentioned in references of these ideas.
>
> 1) FrameNet: Identify frames (indicating a particular type of situation
> along with its participants, i.e. task, doer and props), and then identify
> Logical Units, and their associated Frame Elements by using models trained
> primarily on crowd-sourced data. Primarily used for Automatic Semantic Role
> Labeling.
>
> 2) Babelfy: Using a wide semantic network, encoding structural and lexical
> information of both type encyclopedic and lexicographic like Wikipedia and
> WordNet resp., we can also accomplish our tasks (EL and WSD). In this a
> graphical method along with some heuristics is used to extract out the most
> relevant meaning from the text.
>
> 3) Word2vec / Glove - Methods for designing word vectors based on the
> context. These are primarily employed for WSD.
>
> Moreover if those problems are solved then we can address keyword search
> (5.9) and Confidence Scoring (5.19) effectively as both require association
> of entities to the raw text which will provide concerned entity and its
> attributes to search with and the confidence score.
>
> So I would like to work on 5.16 or 5.17 which will encompass those two
> tasks (EL and WSD) and for this I would like to ask which method will be
> the best for these two tasks? According to me it is the babelfy method
> which will be appropriate for both of these tasks.
>
> Thanks,
> Abhishek Gupta
> On Feb 23, 2015 5:46 PM, "Thiago Galery" <tgal...@gmail.com> wrote:
>
>> Hi Abishek, if you are interested in contributing to any DBpedia project
>> or participating in Gsoc this year it might be a good idea to take a look
>> at this page http://wiki.dbpedia.org/gsoc2015/ideas . This might help
>> you to specify how/where you can contribute. Hope this helps,
>> Thiago
>>
>> On Sun, Feb 22, 2015 at 2:09 PM, Abhishek Gupta <a.gu...@gmail.com>
>> wrote:
>>
>>> Hi all,
>>>
>>> I am Abhishek Gupta. I am a student of Electrical Engineering from IIT
>>> Delhi. Recently I have worked on the projects related to Machine Learning
>>> and Natural Language Processing (i.e. Information Extraction) in which I
>>> extracted Named Entities from raw text to populate knowledge base with new
>>> entities. Hence I am inclined to work in this area. Besides this I am also
>>> familiar with programming languages like C, C++ and Java primarily.
>>>
>>> So I presume that I can contribute a lot towards extracting structured
>>> data from wikipedia which is one of the primary step towards Dbpedia's
>>> primary goal.
>>>
>>> So can anyone please help me out where to start from so as to contribute
>>> towards this?
>>>
>>> Regards
>>> Abhishek Gupta
>>>
>>>
>>> ------------------------------------------------------------------------------
>>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server
>>> from Actuate! Instantly Supercharge Your Business Reports and Dashboards
>>> with Interactivity, Sharing, Native Excel Exports, App Integration & more
>>> Get technology previously reserved for billion-dollar corporations, FREE
>>>
>>> http://pubads.g.doubleclick.net/gampad/clk?id=190641631&iu=/4140/ostg.clktrk
>>> _______________________________________________
>>> Dbpedia-gsoc mailing list
>>> Dbpedia-gsoc@lists.sourceforge.net
>>> https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
>>>
>>>
>>
>
> ------------------------------------------------------------------------------
> Dive into the World of Parallel Programming The Go Parallel Website,
> sponsored
> by Intel and developed in partnership with Slashdot Media, is your hub for
> all
> things parallel software development, from weekly thought leadership blogs
> to
> news, videos, case studies, tutorials and more. Take a look and join the
> conversation now. http://goparallel.sourceforge.net/
> _______________________________________________
> Dbpedia-gsoc mailing list
> Dbpedia-gsoc@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
>
>
------------------------------------------------------------------------------
Dive into the World of Parallel Programming The Go Parallel Website, sponsored
by Intel and developed in partnership with Slashdot Media, is your hub for all
things parallel software development, from weekly thought leadership blogs to
news, videos, case studies, tutorials and more. Take a look and join the
conversation now. http://goparallel.sourceforge.net/
_______________________________________________
Dbpedia-gsoc mailing list
Dbpedia-gsoc@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
