abhidipbhattacharyya / verbnet-parser

VerbNet semantic parser and related utilities

SemParse

SemParse provides common utilities for interfacing with lexical resources in the SemLink project, as well as tools for parsing and extraction. This includes a VerbNet semantic parser, which produces VerbNet thematic roles and semantic predicates.

See a demo of the VerbNet parser in action here!

Quickstart with Docker

A Docker image for running the VerbNet parser demo and API is available here: https://hub.docker.com/r/jgung/verbnet-parser.

docker pull jgung/verbnet-parser:0.1-SNAPSHOT
docker run -p 8080:8080 jgung/verbnet-parser:0.1-SNAPSHOT

Then just open localhost:8080 in your browser, or call the API directly:

curl -s localhost:8080/predict/semantics?utterance=John%20gave%20Mary%20the%20book | python -m json.tool

NOTE: this API has no stability guarantees and will likely change in later versions.

Requirements for Development

• Java 8 and Apache Maven
• For development in an IDE such as IntelliJ IDEA or Eclipse, the corresponding Lombok plugin is required:
  • https://projectlombok.org/setup/eclipse
  • https://projectlombok.org/setup/intellij

semparse-core

Provides a VerbNet parser that uses VerbNet class predictions and PropBank semantic roles to align to a VerbNet frame and produce VerbNet semantic representations.

To use the parser, you will need to download and unzip the pre-trained models and mapping files.

The API is a work in progress (as the project itself is not stable), but an overview of current usage is shown here:

import io.github.clearwsd.parser.*;
import io.github.semlink.verbnet.*;
import io.github.semlink.parser.*;
import io.github.semlink.propbank.type.PropBankArg;
import io.github.semlink.semlink.VerbNetAligner;

import static io.github.semlink.parser.VerbNetParser.pbRoleLabeler;

public class VerbNetParserTest {

    public static void main(String[] args) {
        // VerbNet index over VerbNet classes/frames
        VnIndex verbNet = new DefaultVnIndex();

        // Dependency parser used for WSD model and alignment logic
        NlpParser dependencyParser = new Nlp4jDependencyParser();
        // WSD model for predicting VerbNet classes (uses ClearWSD and the NLP4J parser)
        VerbNetSenseClassifier classifier = VerbNetSenseClassifier.fromModelPath("semparse/nlp4j-verbnet-3.3.bin",
                verbNet, dependencyParser);
        // PropBank semantic role labeler from a TF NLP saved model
        SemanticRoleLabeler<PropBankArg> roleLabeler = pbRoleLabeler("semparse/propbank-srl");
        // maps nominal predicates with light verbs to VerbNet classes (e.g. take a bath -> dress-41.1.1)
        LightVerbMapper verbMapper = LightVerbMapper.fromMappingsPath("semparse/lvm.tsv", verbNet);
        // aligner that uses PropBank VerbNet mappings and heuristics to align PropBank roles with VerbNet thematic roles
        VerbNetAligner aligner = VerbNetAligner.of("semparse/pbvn-mappings.json", "semparse/unified-frames.bin");
        VnPredicateDetector predicateDetector = new DefaultVnPredicateDetector(classifier, verbMapper);

        // simplifying facade over the above components
        VerbNetParser parser = new VerbNetParser(predicateDetector, classifier, roleLabeler, aligner);

        VerbNetParse parse = parser.parse("John ate an apple");
        System.out.println(parse); // Take In[EVENT(E1 = VnClassXml(verbNetId=eat-39.1)), Agent(A0[John]), Patient(A1[an apple])]
    }

}

semparse-tf4j

Wrapper for Tensorflow Java API to load and make predictions with TF-based NLP sequence models exported as saved models from TF-NLP.

semparse-web

A Spring Boot web app with a React frontend to demonstrate VerbNet parsing models.

To build and run the demo yourself, you'll need to copy the pre-trained models and mapping files into the resources folder:

# (download and unzip models as described above into semparse/ directory)
cd verbnet-parser
cp -R semparse/* semparse-web/src/main/resources/
mvn clean install -DskipTests
cd semparse-web
mvn spring-boot:run

Then just open localhost:8080 in your browser.

Try the demo here!