Hello, everyone.

Could you answer the following questions to clarify the project "Fact
Extraction from Wikipedia Text" and could you suggest the existing
warm-up tasks related to this project, if exist?
(I've chosen Relation Extraction as my master thesis topic and I would
like to combine it with this project, i.e. this project would be
practical part of my master thesis, If I could.)

Firstly, just a confirmation, is it the aim of this project that we
enrich the existing datasets? For instance, given the thing in [1], do
we finally add new facts about the thing by using the developed
framework?

Secondly, is it fixed that we use FrameNet for the fact extraction?
Could I use other approaches, such as Distant Supervision[2][3][4], to
extract facts if they are proper?

Finally, if we use FrameNet, how about you consider following 3 points?
1. As far as I understand, the project description shows only the flow
of extracting frames from the source text. How should I use the
frames? Do we directly use the frames as new relations between the
entities, or that we convert the Frames to existing DBpedia's RDF
properties (such as dbpedia-owl:successor) after the extraction?

2. On the step "Verb extraction and ranking", which verbs is the
target of "ranking"?
All of the verbs in the article, as Peresa said? Or can I propose other targets?

3. Creating frames seems to depend on each language. Will I develop
the framework on English articles?

Thank you and regards,
Kenji Yamauchi

[1] http://www.dbpedia.org/page/It%C5%8D_Hirobumi
[2] http://nlp.stanford.edu/software/mimlre.shtml
[3] http://nlp.stanford.edu/pubs/emnlp2012-mimlre.pdf

---------
Kenji Yamauchi
Master's course on Kyoto University

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