Hi Kasun,
The sentence substrings identified via entity linking will be the fe
candidates.
Then I think you got the idea behind the verb ranking step.
Cheers!
----- Reply message -----
Da: "kasun perera" <[email protected]>
A: "Marco Fossati" <[email protected]>
Cc: "dbpedia-gsoc" <[email protected]>
Oggetto: GSOC_2015 Fact Extraction from Wikipedia Text
Data: mer, mar 4, 2015 07:08
Hi Marco
On Mon, Mar 2, 2015 at 5:23 PM, Marco Fossati <[email protected]> wrote:
2- Also it mentioned the use of NLP techniques to process Wikipedia
text. Does this means extraction of Dependency relationships to get the
frame elements (FE) and lexical unit(LU)?
Dependency parsing may not be needed, since entity linking can be applied to
fulfill the task.
I'm not clear what you mean by use of entity-linking to identify FE candidates.
In general Named entity linking (NEL) means linking the mentions of entities in
text to a central knowledge base(e.g. Wikipedia). Do you mean to use the above
concept to find FE's? Can you please clarify bit more on use of entity-linking
to identify FE's?
This is the my understanding of the step-1 of the idea i.e. Verb extraction and
Ranking.We use a list of domains (e.g. Sports) then dig in to more specific
sub-domain (e.g. Soccer, Cricket, Rugby ect) of Wikipedia. The navigate to
specific wiki-pages under the sub domain. For each wiki page we extract and
rank the verbs based on the sub-domain and higher ranked verbs are used as
LU's.What are your comments about this idea?
Thanks
--
Regards
Kasun Perera
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