Sorry, hit sent before finishing the mail :). So, you will disambiguate it using wordnet like this : http://wordnetweb.princeton.edu/perl/webwn?s=attack&sub=Search+WordNet&o2=&o0=1&o8=1&o1=1&o7=&o5=&o9=&o6=&o3=&o4=&h=000000
And then you would have a rule file which would contain something like : event name= "attack" event trigger= wordnet class of type = wordnet id && pos=verb agent=dependency_type:nsubj&&entity_type=Person||Location patient=dependency_type:dobj&&entity_type=Person||Location The dependecy type points to the Stanford NLP dependency tree relation types described here: http://nlp.stanford.edu/software/stanford-dependencies.shtml The entity_type points to either the NER class or the wordnet class for the noun in the noun phrase. This approach was inspired by this paper : http://www.surdeanu.info/mihai/papers/acl2015.pdf with the difference that I'm using WSD to disambiguate the event trigger. I'll start doing some experiments with this approach. On Sun, Sep 20, 2015 at 4:14 PM, Cristian Petroaca < cristian.petro...@gmail.com> wrote: > Hi Dileepa, > > I've been thinking more about the approach using a Word Sense > Disambiguation tool to classify the verb in the sentence and I think it may > be a good approach. The verb seems to be the event trigger and once you > know its actual meaning (by applying a Wordnet class or some other DB used > for WSD) then I think it's quite straightforward to identify the actors in > the event (agent, patient, instrument, etc) by applying some user defined > rules for that verb class. > > For example if you have the verb "attack" which can have multiple meanings > depending on the context you will disambiguate it using wordnet like this: > > On Wed, Sep 9, 2015 at 8:33 PM, Dileepa Jayakody < > dileepajayak...@gmail.com> wrote: > >> Hi Cristian, >> >> Interesting ideas. Let me do some background reading on this, so I can >> also >> participate in the discussion better. >> >> Thanks, >> Dileepa >> >> On Wed, Sep 9, 2015 at 3:17 PM, Cristian Petroaca < >> cristian.petro...@gmail.com> wrote: >> >> > Another approach to this would be to use a semantic role labeling tool >> [1] >> > to determine the type of relation between the subject and object. >> > >> > Or we could use Word Sense Disambiguation to determine the wordnet >> class of >> > the verb (this way we have a standard relation definition) and based on >> > what relation type it is we can search for the subject and object using >> > dependency tree parsing in Stanford NLP. >> > >> > These 2 options ensure that we can have a much bigger recall but I'm not >> > sure about the precision... >> > >> > So I think we'll need to first settle on the method of implementing this >> > engine before starting anything. >> > >> > [1] http://cogcomp.cs.illinois.edu/page/demo_view/srl >> > >> > On Tue, Sep 8, 2015 at 11:45 AM, Cristian Petroaca < >> > cristian.petro...@gmail.com> wrote: >> > >> > > Hi Dileepa, >> > > >> > > Unfortunately I did not have the time to work on this at all so there >> is >> > > no code base . But I'd be happy to start contributing with something >> to >> > > this engine and I think it would also be very helpful if you will be >> able >> > > to contribute to this as well. >> > > I did get a chance to test the Stanford relation extractor which works >> > > fine but it's quite limited to a handful of relation types (live_in, >> > > located_in, org_based_in, work_for). So we would need to train other >> > models >> > > if we want to increase the relation type number. >> > > I also think that the Event Extraction Engine should work in >> conjunction >> > > with any coreference and comention engines we have to increase the >> > relation >> > > count. >> > > >> > > Regards, >> > > Cristian >> > > >> > > On Tue, Sep 8, 2015 at 11:19 AM, Dileepa Jayakody < >> > > dileepajayak...@gmail.com> wrote: >> > > >> > >> Hi Cristian and all, >> > >> >> > >> Can I please know the status of this event extraction engine? Event >> > >> extraction is a really useful feature for semantic enhancements and >> I am >> > >> interested in collaborating with this work. >> > >> Is there any code base you are currently working on for this engine >> > work? >> > >> >> > >> Thanks, >> > >> Dileepa >> > >> >> > >> On Tue, Feb 17, 2015 at 9:10 PM, Cristian Petroaca < >> > >> cristian.petro...@gmail.com> wrote: >> > >> >> > >> > Hi Edi, >> > >> > >> > >> > Thanks for the info. Stanford Relation Extractor sounds very >> > >> interesting. >> > >> > I'll give it a try. >> > >> > >> > >> > 2015-02-17 17:00 GMT+02:00 Edi Bice <edi_b...@yahoo.com.invalid>: >> > >> > >> > >> > > Hi Cristian, >> > >> > > Here are a few more resources on Semantic Role/Relationship >> > Labeling: >> > >> > > 1. FrameNet, VerbNet and WordNet on the data side2. Shalmaneser, >> > >> SEMAFOR >> > >> > > and Stanford Relation Extractor on the code side >> > >> > > The last one links to a great paper which I believe holds great >> > >> potential >> > >> > > for Stanbol: >> > >> > > A Linear Programming Formulation for Global Inference in Natural >> > >> Language >> > >> > > Tasks >> > >> > > >> > >> > > | | >> > >> > > | | | | | | >> > >> > > | A Linear Programming Formulation for Global Inference in >> Natural >> > >> > > Language Tasks Last abstract |Contents |Next abstract A Linear >> > >> > Programming >> > >> > > Formulation for Global Inference in Natural Language Tasks | >> > >> > > | | >> > >> > > | View on www.cnts.ua.ac.be | Preview by Yahoo | >> > >> > > | | >> > >> > > | | >> > >> > > >> > >> > > >> > >> > > >> > >> > > Edi >> > >> > > From: Cristian Petroaca <cristian.petro...@gmail.com> >> > >> > > To: dev@stanbol.apache.org >> > >> > > Sent: Sunday, February 15, 2015 6:34 AM >> > >> > > Subject: Event Extraction Engine >> > >> > > >> > >> > > Hi All, >> > >> > > >> > >> > > Quite a while ago I started a discussion on this list about Event >> > >> > > Extraction from text. See >> > >> > > https://issues.apache.org/jira/browse/STANBOL-1121 >> > >> > > . >> > >> > > >> > >> > > I'd like to get started on the actual work and I have been >> thinking >> > >> how >> > >> > to >> > >> > > best approach this and there are some things that I would do >> > >> differently >> > >> > > than what the JIRA describes.I'd like to get your feedback on it. >> > >> > > >> > >> > > Basically the main approach would be: >> > >> > > >> > >> > > 1. Detect all NERs and their co-references. >> > >> > > >> > >> > > 2. Apply semantic role labeling on the sentences where the above >> > >> > mentioned >> > >> > > NERs reside. >> > >> > > I found some interesting Semantic Role labeling libraries such as >> > >> > > https://code.google.com/p/mate-tools/ or >> > >> > > http://cogcomp.cs.illinois.edu/page/software_view/SRL. >> > >> > > With this I'll be able to detect the Agent, the Verb (action) and >> > the >> > >> > > Patient and Instruments. >> > >> > > >> > >> > > This could be a minimal implementation of the engine. After that >> I >> > can >> > >> > > simply create the event data model as described in the JIRA and >> > >> annotate >> > >> > > the text. >> > >> > > But this does not actually detect what kind of event it is or >> what >> > are >> > >> > the >> > >> > > event specific roles that the entities have in the relation. >> > >> > > >> > >> > > For example we can have the sentence "Google buys Yahoo for $100 >> > >> > million". >> > >> > > There are a lot more to be said about this sentence than simply >> that >> > >> > > "Google" is the agent and "Yahoo" is the Patient. This is >> actually >> > an >> > >> > > acquisition event and "Google" is the buyer and "Yahoo" the >> bought >> > >> > entity. >> > >> > > We also would need to align to a common ontology synonym phrases >> > such >> > >> as >> > >> > > "buy" or "acquire" so that we know that both refer to the same >> > >> > Acquisition >> > >> > > event. >> > >> > > >> > >> > > Having said that, we would add a new step : >> > >> > > 3. Try to detect event type and event details. >> > >> > > >> > >> > > This can be done by either: >> > >> > > >> > >> > > 3.1 Rule based : hand written rules which would map a certain >> > sentence >> > >> > > structure, such as the name of the verb and the type of entities >> as >> > >> > agent, >> > >> > > patient to a certain event type. >> > >> > > This has the benefit of being easy to build but quite inflexible. >> > >> > > >> > >> > > 3.2 Statistical based: train a model which would be able to >> classify >> > >> an >> > >> > > event type based on the features of the sentence such as verb >> type, >> > >> > entity >> > >> > > type, role type, etc.. This is the approach described here : >> > >> > > http://web.stanford.edu/~jurafsky/mintz.pdf. >> > >> > > This would be quite hard to build but quite flexible. >> > >> > > >> > >> > > This 3rd step of detecting event types & details I think would be >> > most >> > >> > > efficient for domain specific events. We would have configs with >> > >> several >> > >> > > models for several domains available and the user could with use >> one >> > >> of >> > >> > the >> > >> > > pre-existent models or create a new one. >> > >> > > >> > >> > > I don't have any practical experience with training models or >> text >> > >> > > classification based on features (but I've been doing a lot of >> > >> reading on >> > >> > > it) so I'm not sure exactly how feasible what I said at point no >> 3 >> > >> > actually >> > >> > > is. >> > >> > > >> > >> > > Regards, >> > >> > > Cristian >> > >> > > >> > >> > > >> > >> > > >> > >> > > >> > >> > >> > >> >> > > >> > > >> > >> > >
