stanfordnlp/CoreNLP · error · RuntimeException

RuntimeException wrapping Exception (dataset read failure)

Error message

RuntimeException wrapping Exception (dataset read failure)

What it means

MetadataWriter's constructor reads the serialized mention-pair dataset (StatisticalCorefTrainer.datasetFile) via IOUtils.readObjectFromFile and wraps any Exception in a plain RuntimeException(e) — no message, only the cause. This is the metadata-writing stage of statistical coref training failing to load its input dataset.

Solutions

Run the dataset-creation stage first so datasetFile exists
Verify StatisticalCorefTrainer.datasetFile points at the correct file for this run
Use a matching CoreNLP version for deserialization to avoid class-incompatibility errors
Inspect the wrapped cause (e.getCause()) — FileNotFoundException vs InvalidClassException vs IOException — and fix accordingly

Example fix

// before
} catch (Exception e) {
  throw new RuntimeException(e);
}
// after
} catch (Exception e) {
  throw new RuntimeException("Error reading mention-pair dataset from "
      + StatisticalCorefTrainer.datasetFile, e);
}

Defensive patterns

Strategy: try-catch

Validate before calling

// ensure dataset exists before constructing MetadataWriter
if (StatisticalCorefTrainer.datasetFile == null)
  throw new IllegalStateException("datasetFile not set — run dataset build stage first");
java.io.File f = new java.io.File(datasetPath);
if (!f.isFile() || !f.canRead()) throw new IllegalStateException("Dataset missing: " + datasetPath);

Try / catch

try {
  MetadataWriter mw = new MetadataWriter(props, dictionaries);
} catch (RuntimeException e) {
  Throwable cause = e.getCause();
  if (cause instanceof java.io.FileNotFoundException)
    throw new IllegalStateException("Dataset not built yet", e);
  if (cause instanceof java.io.InvalidClassException)
    throw new IllegalStateException("CoreNLP version mismatch for serialized dataset", e);
  throw e;
}

Prevention

Build the mention-pair dataset before running MetadataWriter
Serialize and deserialize with the same CoreNLP version
Verify dataset path configuration at startup
Unwrap e.getCause() to identify missing-file vs class-incompatibility issues

When it happens

Trigger: Constructing MetadataWriter when datasetFile is missing/unreadable, or the object stream is corrupt or incompatible (wrong CoreNLP version / class changes).

Common situations: Running the metadata writer before the dataset was built; CoreNLP version mismatch causing InvalidClassException during deserialization; file moved or path misconfigured.

Understand the failure class

Background: "failed to read file", EACCES, ENOENT and "could not read <path>" errors: when a program can't read a file from disk — this error's family across 49 libraries.

Related errors

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/a9bdb848649ccd24. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/coref/statistical/MetadataWriter.java:40

 * Writes various pieces of information about coreference documents to disk.
 * @author Kevin Clark
 */
public class MetadataWriter implements CorefDocumentProcessor {
  private final Map<Integer, Map<Integer, String>> mentionTypes;
  private final Map<Integer, List<List<Integer>>> goldClusters;
  private final Counter<String> wordCounts;
  private final Map<Integer, Map<Pair<Integer, Integer>, Boolean>> mentionPairs;
  private final boolean countWords;

  public MetadataWriter(boolean countWords) {
    this.countWords = countWords;
    mentionTypes = new HashMap<>();
    goldClusters = new HashMap<>();
    wordCounts = new ClassicCounter<>();
    try {
      mentionPairs = IOUtils.readObjectFromFile(StatisticalCorefTrainer.datasetFile);
    } catch (Exception e) {
      throw new RuntimeException(e);
    }
  }

  @Override
  public void process(int id, Document document) {
    // Mention types
    mentionTypes.put(id, document.predictedMentionsByID.entrySet().stream().collect(
        Collectors.toMap(Map.Entry::getKey, e -> e.getValue().mentionType.toString())));

    // Gold clusters
    List<List<Integer>> clusters = new ArrayList<>();
    for (CorefCluster c : document.goldCorefClusters.values()) {
        List<Integer> cluster = new ArrayList<>();
        for (Mention m : c.getCorefMentions()) {
            cluster.add(m.mentionID);
        }
        clusters.add(cluster);
    }

View on GitHub (pinned to 1b7edd19c4)