stanfordnlp/CoreNLP · error · IllegalArgumentException

Unknown argument

Error message

Unknown argument 

What it means

SentimentPipeline.main parses command-line arguments in a loop; when an argument matches none of the known flags, it logs, prints help, and throws IllegalArgumentException("Unknown argument " + ...). Note the message reads args[argIndex + 1], so it may show the wrong token, but the offending flag is args[argIndex].

Solutions

1. Run with -help and use only the listed flags for SentimentPipeline.
2. Correct the misspelled flag (e.g. -file, -fileList, -stdin, -model, -output, -input).
3. Remove extra positional arguments; all inputs must go through -file, -fileList, or -stdin.
4. In wrapper scripts, echo the full command and verify each flag against the -help output; note the error text may name the token after the bad flag, so check the preceding argument too.

Example fix

// before
java edu.stanford.nlp.sentiment.SentimentPipeline -outputFormat scores -file in.txt
// after
java edu.stanford.nlp.sentiment.SentimentPipeline -output scores -file in.txt

Set<String> knownFlags = new HashSet<>(Arrays.asList("-model","-file","-fileList","-stdin","-output","-input","-help","-tokenizerModel","-taggerModel"));
for (String a : args) {
  if (a.startsWith("-") && !knownFlags.contains(a)) {
    throw new IllegalArgumentException("Unsupported flag for SentimentPipeline: " + a);
  }
}

try {
  SentimentPipeline.main(args);
} catch (IllegalArgumentException e) {
  if (e.getMessage().startsWith("Unknown argument")) {
    // the message may show args[i+1]; re-check args[i-1] and args[i]
    System.err.println("Inspect all flags against -help; bad flag likely precedes: " + e.getMessage());
  } else throw e;
}

When it happens

Trigger: Invoking the pipeline with a flag not in its accepted set (e.g. -outputFormat instead of -output, -inputFile instead of -file), or passing a bare positional argument that isn't a recognized option.

Common situations: Copy-pasting flags from StanfordCoreNLP into SentimentPipeline (the flag sets differ); typos like -model vs -mod; scripts passing an extra positional argument; quoting mistakes that split flags.

Understand the failure class

Background: "Unknown argument", "Invalid value", and "must be one of": invalid CLI argument errors explained — this error's family across 35 libraries.

Related errors

• Unknown argument:
• Unknown argument " + args[argIndex]
• Unknown argument " + args[argIndex]
• Unknown output format
• Unknown format

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/dbd4b9b9630a9e43. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/sentiment/SentimentPipeline.java:298

        String[] formats = args[argIndex + 1].split(",");
        outputFormats = new ArrayList<>();
        for (String format : formats) {
          outputFormats.add(Output.valueOf(format.toUpperCase(Locale.ROOT)));
        }
        argIndex += 2;
      } else if (args[argIndex].equalsIgnoreCase("-filterUnknown")) {
        filterUnknown = true;
        argIndex++;
      } else if (args[argIndex].equalsIgnoreCase("-tlppClass")) {
        tlppClass = args[argIndex + 1];
        argIndex += 2;
      } else if (args[argIndex].equalsIgnoreCase("-help")) {
        help();
        System.exit(0);
      } else {
        log.info("Unknown argument " + args[argIndex + 1]);
        help();
        throw new IllegalArgumentException("Unknown argument " + args[argIndex + 1]);
      }
    }

    // We construct two pipelines.  One handles tokenization, if
    // necessary.  The other takes tokenized sentences and converts
    // them to sentiment trees.
    Properties pipelineProps = new Properties();
    Properties tokenizerProps = null;
    if (sentimentModel != null) {
      pipelineProps.setProperty("sentiment.model", sentimentModel);
    }
    if (parserModel != null) {
      pipelineProps.setProperty("parse.model", parserModel);
    }
    if (inputFormat == Input.TREES) {
      pipelineProps.setProperty("annotators", "binarizer, sentiment");
      pipelineProps.setProperty("customAnnotatorClass.binarizer", "edu.stanford.nlp.pipeline.BinarizerAnnotator");
      pipelineProps.setProperty("binarizer.tlppClass", tlppClass);

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