I created a git repository which contains the event extraction engine here https://github.com/cpetroaca/stanbol-event-extraction-engine. I've started working on an event rule schema that will also incorporate a generic ontology definition schema so that one can say that #Person= http://dbpedia.org/Person and then use #Person in the rules. I think that because Stanbol has access to a dbpedia or yago index will be of great value when we want to define events with specific object classes.
Dileepa, if you still want to get involved, you can take a look at the Stanbol Stanford NLP project here https://github.com/westei/stanbol-stanfordnlp and figure out how to add Collapsed Dependencies( http://nlp.stanford.edu/software/dependencies_manual.pdf) to it. We'll need them to sort out the subject, verb and objects. Thanks, Cristian On Mon, Oct 12, 2015 at 3:31 PM, Cristian Petroaca < cristian.petro...@gmail.com> wrote: > Can we get a separate branch where we can start developing the Event > Extraction engine? > > Thanks > > On Sun, Sep 20, 2015 at 4:26 PM, Cristian Petroaca < > cristian.petro...@gmail.com> wrote: > >> 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 >>>> > >> > > >>>> > >> > > >>>> > >> > > >>>> > >> > > >>>> > >> > >>>> > >> >>>> > > >>>> > > >>>> > >>>> >>> >>> >> >
