Train model using Named entity

Question

I am looking on standford corenlp using the Named Entity REcognizer.I have different kinds of input text and i need to tag it into my own Entity.So i started training my own model and it doesnt seems to be working.

For eg: my input text string is "Book of 49 Magazine Articles on Toyota Land Cruiser 1956-1987 Gold Portfolio http://t.co/EqxmY1VmLg http://t.co/F0Vefuoj9Q"

I go through the examples to train my own models and and look for only some words that I am interested in.

My jane-austen-emma-ch1.tsv looks like this

Toyota  PERS
Land Cruiser    PERS

From the above input text i am only interested in those two words. The one is Toyota and the other word is Land Cruiser.

The austin.prop look like this

trainFile = jane-austen-emma-ch1.tsv
serializeTo = ner-model.ser.gz
map = word=0,answer=1
useClassFeature=true
useWord=true
useNGrams=true
noMidNGrams=true
useDisjunctive=true
maxNGramLeng=6
usePrev=true
useNext=true
useSequences=true
usePrevSequences=true
maxLeft=1
useTypeSeqs=true
useTypeSeqs2=true
useTypeySequences=true
wordShape=chris2useLC

Run the following command to generate the ner-model.ser.gz file

java -cp stanford-corenlp-3.4.1.jar edu.stanford.nlp.ie.crf.CRFClassifier -prop austen.prop

public static void main(String[] args) {
        String serializedClassifier = "edu/stanford/nlp/models/ner/english.muc.7class.distsim.crf.ser.gz";
        String serializedClassifier2 = "C:/standford-ner/ner-model.ser.gz";
        try {
            NERClassifierCombiner classifier = new NERClassifierCombiner(false, false, 
                    serializedClassifier2,serializedClassifier);
            String ss = "Book of 49 Magazine Articles on Toyota Land Cruiser 1956-1987 Gold Portfolio http://t.co/EqxmY1VmLg http://t.co/F0Vefuoj9Q";
            System.out.println("---");
            List<List<CoreLabel>> out = classifier.classify(ss);
            for (List<CoreLabel> sentence : out) {
              for (CoreLabel word : sentence) {
                System.out.print(word.word() + '/' + word.get(AnswerAnnotation.class) + ' ');
              }
              System.out.println();
            }

        } catch (ClassCastException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }  catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }

    }

Here is the output I am getting

Book/PERS of/PERS 49/O Magazine/PERS Articles/PERS on/O Toyota/PERS Land/PERS Cruiser/PERS 1956-1987/PERS Gold/O Portfolio/PERS http://t.co/EqxmY1VmLg/PERS http://t.co/F0Vefuoj9Q/PERS

which i think its wrong.I am looking for Toyota/PERS and Land Cruiser/PERS(Which is a multi valued fied.

Thanks for the Help.Any help is really appreciated.

Answer 1

The NERClassifier* is word level, that is, it labels words, not phrases. Given that, the classifier seems to be performing fine. If you want, you can hyphenate words that form phrases. So in your labeled examples and in your test examples, you would make "Land Cruiser" to "Land_Cruiser".

Answer 2

I believe you should also put examples of 0 entities in your trainFile. As you gave it, the trainFile is just too simple for the learning to be done, it needs both 0 and PERSON examples so it doesn't annotate everything as PERSON. You're not teaching it about your not-of-interest entities. Say, like this:

Toyota  PERS
of    0
Portfolio    0
49    0

and so on.

Also, for phrase-level recognition you should look into regexner, where you can have patterns (patterns are good for us). I'm working on this with the API and I have the following code:

Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma, ner, regexner");
props.put("regexner.mapping", customLocationFilename);

with the following customLocationFileName:

Make Believe Town   figure of speech    ORGANIZATION
( /Hello/ [{ ner:PERSON }]+ )   salut   PERSON
Bachelor of (Arts|Laws|Science|Engineering) DEGREE
( /University/ /of/ [{ ner:LOCATION }] )    SCHOOL

and text: Hello Mary Keller was born on 4th of July and took a Bachelor of Science. Partial invoice (€100,000, so roughly 40%) for the consignment C27655 we shipped on 15th August to University of London from the Make Believe Town depot. INV2345 is for the balance.. Customer contact (Sigourney Weaver) says they will pay this on the usual credit terms (30 days).

The output I get

Hello Mary Keller is a salut
4th of July is a DATE
Bachelor of Science is a DEGREE
$ 100,000 is a MONEY
40 % is a PERCENT
15th August is a DATE
University of London is a ORGANIZATION
Make Believe Town is a figure of speech
Sigourney Weaver is a PERSON
30 days is a DURATION

For more info on how to do this you can look at the example that got me going.