Training model ignored by stanford CoreNLP

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By : KARIMI KHADIJA

Date : November 29 2020, 09:01 AM

wish helps you I've made a small, sample training model to use when performing sentiment analysis with coreNLP. In order to get coreNLP to use this model, I've written the following lines of code: , You want this:

code :

props.put("sentiment.model", "/path/to/sample_model-0023-100.00.ser.gz");

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Stanford CoreNLP Training Examples

By : user3924307

Date : March 29 2020, 07:55 AM

should help you out Those files are the training data for the MUC-6 and MUC-7 tasks:
http://cs.nyu.edu/faculty/grishman/muc6.html

How to create a Stanford coreNLP model by training?

By : Autumn Chen

Date : March 29 2020, 07:55 AM

Hope that helps You need to include the CoreNLP jar file in your classpath. So, your java command should look like:
java -cp /path/to/corenlp/jar:/path/to/corenlp/library/dependencies -mx8g ...

How can I effectively build a sentiment model training dataset using Stanford CoreNLP?

By : SapinderPal Singh

Date : March 29 2020, 07:55 AM

wish help you to fix your issue Here is some sample code I wrote to go through a tree and print out every subtree. So to get the print out you want just use the printSubTrees method I wrote and have it print out everything in your sentiment tree.

code :

import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.Word;
import edu.stanford.nlp.parser.lexparser.LexicalizedParser;
import edu.stanford.nlp.parser.lexparser.TreeBinarizer;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.trees.*;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Properties;

public class SubTreesExample {

    public static void printSubTrees(Tree inputTree) {
        ArrayList<Word> words = new ArrayList<Word>();
        for (Tree leaf : inputTree.getLeaves()) {
            words.addAll(leaf.yieldWords());
        }
        System.out.print(inputTree.label()+"\t");
        for (Word w : words) {
            System.out.print(w.word()+ " ");
        }
        System.out.println();
        for (Tree subTree : inputTree.children()) {
            printSubTrees(subTree);
        }
    }

    public static void main(String[] args) {
        Properties props = new Properties();
        props.setProperty("annotators", "tokenize,ssplit,pos,lemma,ner,parse");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
        String text = "I do not love you.";
        Annotation annotation = new Annotation(text);
        pipeline.annotate(annotation);
        Tree sentenceTree = annotation.get(CoreAnnotations.SentencesAnnotation.class).get(0).get(
                TreeCoreAnnotations.TreeAnnotation.class);
        printSubTrees(sentenceTree);

    }
}

Training caseless NER models with Stanford corenlp

By : Siby Easow

Date : March 29 2020, 07:55 AM

Any of those help There is only one property change in our models, which is that you want to have it invoke a function that removes case information before words are processed for classification. We do that with this property value (which also maps some words to American spelling):
wordFunction = edu.stanford.nlp.process.LowercaseAndAmericanizeFunction

Stanford CoreNLP Classifier: NER training context

By : Simon Hsu

Date : March 29 2020, 07:55 AM

hope this fix your issue Ad 1. Context is helpful if your classification is context-dependent.
Ad 2. Under the hood Stanford CoreNLP Classifier uses CRF (Conditional Random Field) algorithm which uses the order of words to classify correctly as well.