I am using dependency parsing of coreNLP for a project of mine. The basic and enhanced dependencies are different result for a particular dependency. I used the following code to get enhanced dependencies.
val lp = LexicalizedParser.loadModel("edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
lp.setOptionFlags("-maxLength", "80")
val rawWords = edu.stanford.nlp.ling.Sentence.toCoreLabelList(tokens_arr:_*)
val parse = lp.apply(rawWords)
val tlp = new PennTreebankLanguagePack()
val gsf:GrammaticalStructureFactory = tlp.grammaticalStructureFactory()
val gs:GrammaticalStructure = gsf.newGrammaticalStructure(parse)
val tdl = gs.typedDependenciesCCprocessed()
For the following example,
Account name of ramkumar.
I use simple API to get basic dependencies. The dependency i get between (account,name) is (compound). But when i use the above code to get enhanced dependency i get the relation between (account,name) as (dobj).
What is the fix to this? Is this a bug or am i doing something wrong?
When I run this command:
java -Xmx8g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma,ner,parse -file example.txt -outputFormat json
With your example text in the file example.txt
, I see compound
as the relationship between both of those words for both types of dependencies.
I also tried this with the simple API
and got the same results.
You can see what simple
produces with this code:
package edu.stanford.nlp.examples;
import edu.stanford.nlp.semgraph.SemanticGraphFactory;
import edu.stanford.nlp.simple.*;
import java.util.*;
public class SimpleDepParserExample {
public static void main(String[] args) {
Sentence sent = new Sentence("...example text...");
Properties props = new Properties();
// use sent.dependencyGraph() or sent.dependencyGraph(props, SemanticGraphFactory.Mode.ENHANCED) to see enhanced dependencies
System.out.println(sent.dependencyGraph(props, SemanticGraphFactory.Mode.BASIC));
}
}
I don't know anything about any Scala interfaces for Stanford CoreNLP. I should also note my results are using the latest code from GitHub, though I presume Stanford CoreNLP 3.8.0 would also produce similar results. If you are using an older version of Stanford CoreNLP that could be a potential cause of the error.
But running this example in various ways using Java I don't see the issue you are encountering.