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" <[email protected]> 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 <[email protected]> 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
>>
>>
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>>
>>
>
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