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 >> > > >> > > >> > > >> > > >> > >> > >
