Stanford CoreNLP BasicPipelineExample doesn't work

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I'm trying to get started with Stanford CoreNLP and can't even get past the very first simple example from here.

https://stanfordnlp.github.io/CoreNLP/api.html

Here is my code:

package stanford.corenlp;

import java.io.File;
import java.io.IOException;
import java.nio.charset.Charset;
import java.util.List;
import java.util.Map;
import java.util.Properties;

import com.google.common.io.Files;

import edu.stanford.nlp.dcoref.CorefChain;
import edu.stanford.nlp.dcoref.CorefCoreAnnotations.CorefChainAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.NamedEntityTagAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.PartOfSpeechAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.SentencesAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TextAnnotation;
import edu.stanford.nlp.ling.CoreAnnotations.TokensAnnotation;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;
import edu.stanford.nlp.semgraph.SemanticGraph;
import edu.stanford.nlp.semgraph.SemanticGraphCoreAnnotations.CollapsedCCProcessedDependenciesAnnotation;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeCoreAnnotations.TreeAnnotation;
import edu.stanford.nlp.util.CoreMap;
import java.util.logging.Level;
import java.util.logging.Logger;

    private void test2() {
        // creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution
        Properties props = new Properties();
        props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref");
        StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

        // read some text in the text variable
        String text = "Now is the time for all good men to come to the aid of their country.";

        // create an empty Annotation just with the given text
        Annotation document = new Annotation(text);

        // run all Annotators on this text
        pipeline.annotate(document);

    }

  public static void main(String[] args) throws IOException {
      StanfordNLP nlp = new StanfordNLP();
      nlp.test2();
  }

}

Here is the stacktrace:

Adding annotator tokenize
No tokenizer type provided. Defaulting to PTBTokenizer.
Adding annotator ssplit
Adding annotator pos
Exception in thread "main" edu.stanford.nlp.io.RuntimeIOException: Error while loading a tagger model (probably missing model file)
    at edu.stanford.nlp.tagger.maxent.MaxentTagger.readModelAndInit(MaxentTagger.java:791)
    at edu.stanford.nlp.tagger.maxent.MaxentTagger.<init>(MaxentTagger.java:312)
    at edu.stanford.nlp.tagger.maxent.MaxentTagger.<init>(MaxentTagger.java:265)
    at edu.stanford.nlp.pipeline.POSTaggerAnnotator.loadModel(POSTaggerAnnotator.java:85)
    at edu.stanford.nlp.pipeline.POSTaggerAnnotator.<init>(POSTaggerAnnotator.java:73)
    at edu.stanford.nlp.pipeline.AnnotatorImplementations.posTagger(AnnotatorImplementations.java:55)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.lambda$getNamedAnnotators$42(StanfordCoreNLP.java:496)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.lambda$getDefaultAnnotatorPool$65(StanfordCoreNLP.java:533)
    at edu.stanford.nlp.util.Lazy$3.compute(Lazy.java:118)
    at edu.stanford.nlp.util.Lazy.get(Lazy.java:31)
    at edu.stanford.nlp.pipeline.AnnotatorPool.get(AnnotatorPool.java:146)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.construct(StanfordCoreNLP.java:447)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.<init>(StanfordCoreNLP.java:150)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.<init>(StanfordCoreNLP.java:146)
    at edu.stanford.nlp.pipeline.StanfordCoreNLP.<init>(StanfordCoreNLP.java:133)
    at stanford.corenlp.StanfordNLP.test2(StanfordNLP.java:93)
    at stanford.corenlp.StanfordNLP.main(StanfordNLP.java:108)
Caused by: java.io.IOException: Unable to open "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger" as class path, filename or URL
    at edu.stanford.nlp.io.IOUtils.getInputStreamFromURLOrClasspathOrFileSystem(IOUtils.java:480)
    at edu.stanford.nlp.tagger.maxent.MaxentTagger.readModelAndInit(MaxentTagger.java:789)
    ... 16 more
C:\Users\Greg\AppData\Local\NetBeans\Cache\8.2\executor-snippets\run.xml:53: Java returned: 1
BUILD FAILED (total time: 0 seconds)

What am I missing?

stanford-nlp
2 Answers

[2019-12-31] For clarity / reference; note: Linux terminal.

Download CoreNLP 3.9.2 | stanford-corenlp-full-2018-10-05.zip, https://stanfordnlp.github.io/CoreNLP/download.html] and extract it.

pwd; ls -l
  /mnt/Vancouver/apps/CoreNLP/src-local/zzz
  -rw-r--r-- 1 victoria victoria 393239982 Dec 31 14:13 stanford-corenlp-full-2018-10-05.zip

unzip stanford-corenlp-full-2018-10-05.zip
  # ...

ls -l
  drwxrwxr-x 5 victoria victoria      4096 Oct  8  2018 stanford-corenlp-full-2018-10-05
  -rw-r--r-- 1 victoria victoria 393239982 Dec 31 14:13 stanford-corenlp-full-2018-10-05.zip

Save the "BasicPipelineExample.java" code

in a file named BasicPipelineExample.java :

/mnt/Vancouver/apps/CoreNLP/src-local/zzz/BasicPipelineExample.java

Compile it

pwd     ## "sanity check"
  /mnt/Vancouver/apps/CoreNLP/src-local/zzz/

javac -cp stanford-corenlp-3.9.2.jar  BasicPipelineExample.java -Xdiags:verbose

which gives the Java class file BasicPipelineExample.class, and run it from that dir,

java -cp .:* BasicPipelineExample

Addendum

The code above describes access to CoreNLP in the Java environment, as described here: https://stanfordnlp.github.io/CoreNLP/api.html#quickstart-with-convenience-wrappers

For those more inclined (myself included) Stanford provides basically the same functionality in a Python environment, as described here: https://stanfordnlp.github.io/stanfordnlp/corenlp_client.html

E.g.,

import stanfordnlp
from stanfordnlp.server import CoreNLPClient
# JSON output [default]:
client = CoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner', \
    'parse', 'depparse','coref'], timeout=30000, memory='16G')
# Plain-text ourput (much more compact):
client = CoreNLPClient(annotators='tokenize, ssplit, pos, lemma, ner, parse, \
    depparse, coref', output_format='text',  timeout=30000, memory='16G')
text = 'Breast cancer susceptibility gene 1 (BRCA1) is a tumor suppressor protein.'
# This auto-starts the client() instance:
ann = client.annotate(text)
  # ....
sentence = ann.sentence[0]
print(sentence)
  # ... copious output ...
print(ann)
  # ... more succinct ...

Note: if you are using the output_format='text' argument you can do this

print(ann)

but not this

sentence = ann.sentence[0]
print(sentence)
  Traceback (most recent call last):
    File "<console>", line 1, in <module>
  AttributeError: 'str' object has no attribute 'sentence'

Using the stanfordnlp package you can also set up a pipeline, as described here: https://stanfordnlp.github.io/stanfordnlp/

E.g.,

import stanfordnlp
stanfordnlp.download('en')
nlp = stanfordnlp.Pipeline()
text = 'Bananas are an excellent source of potassium.'
text_nlp = nlp(text)
text_nlp.sentences[0].print_dependencies()

Lastly -- though I find the functionality limited (cf. the Stanford-authored CoreNLP libraries) -- somewhat similar results are available by accessing CoreNLP in spaCy: https://github.com/explosion/spacy-stanfordnlp

import stanfordnlp
from spacy_stanfordnlp import StanfordNLPLanguage
snlp = stanfordnlp.Pipeline(lang="en")
nlp = StanfordNLPLanguage(snlp)
doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.")
for token in doc:
    print(token.text, token.lemma_, token.pos_, token.dep_)
  # ...
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