I too support Richard Eckart de Castilho idea for solving that problem. Regards, Rakesh.P
> On 11-Jul-2016, at 8:29 PM, Richard Eckart de Castilho <r...@apache.org> > wrote: > > I don't think that the OpenNLP named entity recognizer itself is able to > generate more than one label. A solution for you would be to train separate > classifiers, one for "person" and one for "director" and add two > OpenNlpNamedEntityRecognizer components to the pipeline, each using of of > your trained models. > > See also: https://github.com/dkpro/dkpro-core/issues/901 > > Cheers, > > -- Richard > >> On 11.07.2016, at 16:53, John Bower <j...@zode64.com> wrote: >> >> I'm currently using OpenNLP with UIMA to label words in a sentence. It's >> important that a single word can be labelled more than once. For example >> David >> Cronenberg should be labelled as director and person. >> >> I know the training process is implemented correctly because I have a >> custom model file and when all sentences with one of the labels is removed >> from the model file the other label is detected. >> >> I would preferably be able to continue to use OpenNLP to double label >> words. Is there a way to do this? If not is this possible with another >> library such as Stanford CoreNLP. >> >> The code that gets the labels is below: >> >> List<NamedEntity> entities = JCasUtil.selectCovered( >> NamedEntity.class, aConstituent ); >> if ( !entities.isEmpty() ) { >> // is never more than 1 >> } >> >> And some sample training data is below (there are hundreds of lines similar >> to this.) >> >> <START:person> David Cronenberg <END> directed <START:film> Crash >> <END> .<START:director> David Cronenberg <END> directed <START:film> >> Scanners <END> . >