Command Line Usage
Table of contents
Note: Stanford CoreNLP v.3.5+ requires Java 8, but works with Java 9/10/11 as well. If using Java 9/10/11, you need to add this Java flag to avoid errors (a CoreNLP library dependency uses the JAXB module that was deleted from the default libraries for Java 9+):
The minimal command to run Stanford CoreNLP from the command line is:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -file input.txt
If this command is run from the distribution directory, it processes the included sample file
input.txt. We use a wildcard
-cp to load all jar files in the current directory – it needs to be in quotes. This command writes the output to an XML file named
input.txt.xml in the same directory.
- Processing a short text like this is very inefficient. It takes a minute to load everything before processing begins. You should batch your processing.
- Current releases of Stanford CoreNLP require Java version 8 or higher.
- Specifying memory: adding, e.g.,
-cpflag specifies the amount of RAM that Java will make available for CoreNLP. On a 64-bit machine, Stanford CoreNLP typically requires 2GB to run (and it may need up to 6GB, depending on the annotators used and the size of the document to parse). On a 32 bit machine (in 2016, this is most commonly a 32-bit Windows machine), you cannot allocate 2GB of RAM; probably you should try with
-Xmx1800mor maybe with just
-Xmx1500m, but this amount of memory is a bit marginal. You probably can’t run some annotators, such as the statistical
coref. Providing your machine has lots of memory, in most cases Java will now start with a large memory allocation and you shouldn’t need to specify this flag manually.
- Stanford CoreNLP includes an interactive shell for analyzing sentences. If you do not specify any properties that load input files, you will be placed in the interactive shell. Type
Your command line has to load the code, libraries, and model jars that CoreNLP uses. These are all contained in JAR files (compressed archives with extension “.jar”) which come in the CoreNLP download or which can be downloaded on demand from Maven Central. The easiest way to make them available is with a command line like this, where
/Users/me/corenlp/ should be changed to the path where you put CoreNLP:
java -cp "/Users/me/corenlp/*" edu.stanford.nlp.pipeline.StanfordCoreNLP -file inputFile
Alternatively, you can [add this path to your CLASSPATH environment variable](https://en.wikipedia.org/wiki/Classpath_(Java%29), so these libraries are always available.
The “*” (which must be enclosed in quotes) says to add all JAR files in the given directory to the classpath. You can also individually specify the needed jar files. Use the following sort of command line, adjusting the JAR file date extensions
VV to your downloaded release.
java -cp stanford-corenlp-VV.jar:stanford-corenlp-VV-models.jar:xom.jar:joda-time.jar:jollyday.jar:ejml-VV.jar edu.stanford.nlp.pipeline.StanfordCoreNLP -file inputFile
The command above works for Mac OS X or Linux. For Windows, the colons (:) separating the jar files need to be semi-colons (;). If you are not sitting in the distribution directory, you’ll also need to include a path to the files before each.
Before using Stanford CoreNLP, it is usual to create a configuration file (a Java Properties file). Minimally, this file should contain the “annotators” property, which contains a comma-separated list of Annotators to use. For example, the setting below enables: tokenization, sentence splitting (required by most Annotators), POS tagging, lemmatization, NER, (constituency) parsing, and (rule-based) coreference resolution.
annotators = tokenize, ssplit, pos, lemma, ner, parse, dcoref
To use the properties in the properties file sampleProps.properties, you give a command as follows:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -props sampleProps.properties
This results in the output file input.txt.output given the same input file
However, if you just want to specify a few properties, you can instead place them on the command line. For example, we can specify annotators and the output format with:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize -file input.txt -outputFormat conll -output.columns word
-props parameter is optional. By default, Stanford CoreNLP will search for StanfordCoreNLP.properties in your classpath and use the defaults included in the distribution.
-annotators argument is also optional. If you leave it out, the code uses a built in properties file, which enables the following annotators: tokenization and sentence splitting, POS tagging, lemmatization, NER, dependency parsing, and statistical coreference resolution:
annotators = tokenize, ssplit, pos, lemma, ner, depparse, coref.
If you have a lot of text but all you want to do is to, say, get part-of-speech (POS) tags, then you should definitely specify an annotators list, as above, since you can then omit later annotators which invoke much more expensive processing that you don’t need. For example, you might give the command:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,pos -file wikipedia.txt -outputFormat conll
We provide a small shell script
corenlp.sh. On Linux or OS X, this may be useful in allowing you to type shorter command lines to invoke CoreNLP. For example, you can instead say:
./corenlp.sh -annotators tokenize,pos -file wikipedia.txt -outputFormat conll
You first have to have available a models jar file for the language you wish to use. You can download it from this site or you can use the models file on Maven Central. If using Maven, you add it to your pom file like this:
<dependency> <groupId>edu.stanford.nlp</groupId> <artifactId>stanford-corenlp</artifactId> <version>3.9.1</version> <classifier>models-chinese</classifier> </dependency>
Our examples assume that you are in the root directory of CoreNLP and that these extra jar files are also available there. Each language jar contains a default properties file for the appropriate language. Working with text in another language is then as easy as specifying this properties file. For example, for Chinese:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -props StanfordCoreNLP-chinese.properties -file chinese.txt -outputFormat text
You can as usual specify details on the annotators and properties:
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP -props StanfordCoreNLP-french.properties -annotators tokenize,pos,depparse -file french.txt -outputFormat conllu
To process one file, use the
-file option followed by a filename. To process a list of files, use the
java -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLP [ -props myprops.props ] -fileList filelist.txt
-fileList parameter points to a file which lists all files to be processed (one per line).
If you do not specify any properties that load input files (and do not specify any input or output redirections), then you will be placed in the interactive shell. Type
q to exit.
If you do not specify an option that loads input files and you redirect either input or output, then Stanford CoreNLP runs as a filter that reads from stdin and writes to stdout. The default mode is line-oriented: Each line of input counts as a document. If you give the flag/property
isOneDocument = true) then the input till end-of-file will be treated as one document.
If your input files have XML tags in them, you may wish to add the
cleanxml annotator to preprocess it. Place it immediately after
If your input is already tokenzed and one sentence per line, then you should use the flags:
Fine point: Stanford CoreNLP treats Unicode end of line markers (LS U+2028 and PS U+2029) as line ends, whereas conventional Unix utilities do not. If these characters are present and you are using CoreNLP in a Unix line-oriented processing pipeline, you may need to remap these characters to ‘\n’ or ‘ ‘ at the start of your processing pipeline.
If (and only if) the input filename ends with “.ser.gz” then CoreNLP will interpret the file as the output of a previous annotation run, to which you presumably want to add on further annotations. CoreNLP will read these Annotations using the class specified in the
inputSerializer property. The options for this are the same as for
outputSerializer below. Note: To successfully load a pipeline for layering on additional annotations, you need to include the property
enforceRequirements = false to avoid complaints about required earlier annotators not being present in the pipeline.
For each input file, Stanford CoreNLP generates one output file, with a name that adds an extra extension to the input filename. (If reading input from stdin, then it will send output to stdout.) The output may contain the output of all annotations that were done, or just a subset of them. For the first example under Quick Start above, with
input.txt containing the text below:
Stanford University is located in California. It is a great university.
Stanford CoreNLP generates this output.
Note that this XML output can use the
CoreNLP-to-HTML.xsl stylesheet file, which comes with the CoreNLP download or can be downloaded from here. This stylesheet enables human-readable display of the above XML content. For example, this example should display like this.
The following properties are associated with output :
-outputDirectory: By default, output files are written to the current directory. You may specify an alternate output directory with the flag
-outputExtension: Output filenames are the same as input filenames but with
-outputExtensionadded to them (the default depends on the
-noClobber: By default, files are overwritten (clobbered). Pass
-noClobberto avoid this behavior.
-replaceExtension: If you’d rather replace the extension with the
-outputExtension, pass the
-replaceExtensionflag. This will result in filenames like
input.txtas an input file).
-outputFormat: Different methods for outputting results. Can be:
- “text”: An ad hoc human-readable text format. Tokens, s-expression parse trees, relation(head, dep) dependencies. Output file extension is
.out. This is the default output format only if the XMLOutputter is unavailable.
- “xml”: An XML format with accompanying XSLT stylesheet, which allows web browser rendering. Output file extension is
.xml. This is the default output format, unless the XMLOutputter is unavailable.
- “json”: JSON. Output file extension is
.json. ’Nuf said.
- “conll”: A tab-separated values (TSV) format. Output extension is
.conll. This output format usually only gives a partial view of an
Annotationand doesn’t correspond to any particular CoNLL format. By default, the columns written are:
deprel. You can customize which fields are written with the
output.columnsproperty. Its value is a comma-separated list of output key names, where the names are ones understood by
AnnotationLookup.KeyLookup. Available names include the seven used in the default output and others such as
speaker. For instance, you can write out just tokenized text, with one token per line and a blank line between sentences by using
-output.columns word. Alternatively, if you give the property
output.prettyPrint = falseto this outputter, it will print one sentence per line output with the selected fields separated by slash (/) characters. You can hence use this option to write tokenized text, one sentence per line with the options
-outputFormat conll -output.columns word -output.prettyPrint false.
- “conllu”: CoNLL-U output format, another tab-separated values (TSV) format, with particular extended features. Output extension is
.conllu. This representation may give only a partial view of an
- “serialized”: Produces some serialized version of each
Annotation. May or may not be lossy. What you actually get depends on the
outputSerializerproperty, which you should also set. The default is the
GenericAnnotationSerializer, which uses the built-in Java object serialization and writes a file with extension
- “text”: An ad hoc human-readable text format. Tokens, s-expression parse trees, relation(head, dep) dependencies. Output file extension is
Other more obscure output options are:
output.includeText: Boolean. Whether to include text of document in document annotations.
output.prettyPrint: Boolean. Whether to pretty print certain annotations (more friendly to humans; less space efficient.
output.constituencyTree: String. Style of constituency tree printing to be used. One known to
output.dependencyTree: String. Style of dependency tree printing to be used. One known to
output.coreferenceContextSize: int. Whether to print some conext around a coreference mention.
output.printSingletonEntities: Boolean. Whether to print singleton entity mentions in coreference output.
output.relationsBeam: double. Whether to filter relations extracted by goodness score.
output.columns: String. Which columns to print in
conlloutput. A list of names like:
The value of the
outputSerializer property is the name of a class which extends
edu.stanford.nlp.pipeline.AnnotationSerializer. Valid choices include:
edu.stanford.nlp.kbp.slotfilling.ir.index.KryoAnnotationSerializer. If unspecified the value of the
serializer property will be tried instead. If it is also not defined, the default is to use
ProtobufAnnotationSerializer is a non-lossy annotation serialization. It uses the Java methods writeDelimitedTo() and parseDelimitedFrom(), which allow sending several length-prefixed messages in one stream. Unfortunately, Google has declined to implement these methods for Python or C++. You can get information from Stack Overflow and other places on how to roll your own version for C++ or Python. Probably the best place is here but there are many other sources of information including: here, here, here, and here. This Stack Overflow question explicitly addresses the issue for CoreNLP.
In all output formats (and in our code), we number sentences and character offsets from 0 and we number tokens from 1. We realize that this is inconsistent! However, it seemed to be the best thing to do. Numbering character offsets from 0 is the only good choice, given how the Java String class and most modern programming languages work, following Dijkstra’s arguments for indexing from 0 (which were influential at the time if not necessarily so water-tight). Numbering tokens from 1 not only corresponds to the human-natural convention (“the first word of the sentence”) but most importantly is consistent with common NLP standards, such as the CoNLL formats used from CoNLL-X through CoNLL 2009, etc., and in CoNLL-U, which number tokens starting from 1. For sentences, we could then choose to be consistent with either but not both of the above. We went with 0-indexing.
CoreNLP’s default character encoding is Unicode’s UTF-8. You can change the encoding used by supplying the program with the command line flag
-encoding FOO (or including the corresponding property in a properties file that you are using). We’ve done a lot of careful work to make sure CoreNLP works with any character encoding supported by Java. Want to use ISO-8859-15 or GB18030? Be our guest!