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 > > > >
