Stanford POS tagger FAQ


  1. What is the tag set used by the Stanford Tagger?
  2. How do I use the API?
  3. Why do I get Exception in thread "main" java.lang.NoClassDefFoundError:edu/stanford/nlp/tagger/maxent/MaxentTagger?
  4. How can I lemmatize (reduce to a base, dictionary form) words that have been tagged with the POS tagger?
  5. How do I have the tagger use pre-tokenized text?
  6. How can I achieve a single jar file deployment of the POS tagger?
  7. Can I run the tagger as a server?
  8. Why am I running out of memory in general?
  9. What different output formats are available?
  10. Is your tagger slow?
  11. How do I train a tagger?
  12. What model should I use?
  13. What is the difference between "english" and "wsj"?
  14. What are the distsim clusters used by the tagger?
  15. How do I tag one pre-tokenized sentence per line?
  16. How do I tag un-tokenized text as one sentence per line?
  17. Why does it crash when I try to optimize with search=owlqn? Is owlqn available anywhere?
  18. How do I output the results to a file?
  19. How do I fix the Stanford POS Tagger giving a NoSuchMethodError or NoSuchFieldError?

Questions with answers

  1. What is the tag set used by the Stanford Tagger?

    You can train models for the Stanford POS Tagger with any tag set. For the models we distribute, the tag set depends on the language, reflecting the underlying treebanks that models have been built from. That is, the tag set was wholly or mainly decided by the treebank producers not us). Here are relevant links:

    Please read the documentation for each of these corpora to learn about their tagsets. You can often also find additional documentation resources by doing web searches.

  2. How do I use the API?

    A brief demo program included with the download will demonstrate how to load the tool and start processing text. When using this demo program, be sure to include all of the appropriate jar files in the classpath.

  3. Why do I get Exception in thread "main" java.lang.NoClassDefFoundError:edu/stanford/nlp/tagger/maxent/MaxentTagger?

    This means your Java CLASSPATH isn't set correctly, so the tagger (in stanford-tagger.jar) isn't being found. See the examples in the README.txt file for how to set the classpath with the -cp or -classpath option. See, for example, for general discussion of the Java classpath.

  4. How can I lemmatize (reduce to a base, dictionary form) words that have been tagged with the POS tagger?

    For English (only), you can do this using the included Morphology class. You can do it with the flag -outputFormatOptions lemmatize. For instance:

    $ java -cp "*" edu.stanford.nlp.tagger.maxent.MaxentTagger -model edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger -textFile samsawme.txt -outputFormat inlineXML -outputFormatOptions lemmatize
    <?xml version="1.0" encoding="UTF-8"?>
    <sentence id="0">
    <word wid="0" pos="NNP" lemma="Sam">Sam</word>
    <word wid="1" pos="VBD" lemma="see">saw</word>
    <word wid="2" pos="PRP" lemma="I">me</word>
    <word wid="3" pos="." lemma=".">.</word>
  5. How do I have the tagger use pre-tokenized text?

    Use the flag "-tokenize false".

  6. How can I achieve a single jar file deployment of the POS tagger?

    You can insert one or more tagger models into the jar file and give options to load a model from there. Here are detailed instructions.

    1. Start in the home directory of the unpacked tagger download
    2. Make a copy of the jar file, into which we'll insert a tagger model:
      cp stanford-postagger.jar stanford-postagger-withModel.jar
    3. Put the model on a path for inclusion in the jar file:
      mkdir -p edu/stanford/nlp/models/pos-tagger/english-left3words
      cp models/english-left3words-distsim.tagger edu/stanford/nlp/models/pos-tagger/english-left3words
    4. Insert one or more models into the jar file - we usually do it under edu/stanford/nlp/models/.
      jar -uf stanford-postagger-withModel.jar edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger
    5. You can now specify loading this model by loading it directly from the classpath.
      java -mx300m -cp stanford-postagger-withModel.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger -textFile sample-input.txt
    6. Or, in code, you can similarly load the tagger like this:
      MaxentTagger tagger = new MaxentTagger("edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger");
    The tagger will load paths in the CLASSPATH in preference to those on the file system.
  7. Can I run the tagger as a server?

    Yes! (This was added in version 2.0.) We provide MaxentTaggerServer as a simple example of a socket-based server using the POS tagger. With a bit of work, we're sure you can adapt this example to work in a REST, SOAP, AJAX, or whatever system. If not, pay us a lot of money, and we'll work it out for you.

    If you're doing this, you may also be interested in single jar deployment. We'll use a continuation of the answer to the previous question in our example (but the two features are independent). The commands shown are for a Unix/Linux/Mac OS X system. For Windows, you reverse the slashes, etc. You start the server on some host by specifying a model and a port for it to run on:

    java -mx300m -cp stanford-postagger-withModel.jar edu.stanford.nlp.tagger.maxent.MaxentTaggerServer -model edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger -port 2020 &
    The same class then includes a demonstration client, which you'll want to adapt to your own needs. You can invoke it like this:
    $ java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTaggerServer -client -host -port 2020
    Input some text and press RETURN to POS tag it, or just RETURN to finish.
    I hope this'll show the server working.
    I_PRP hope_VBP this_DT 'll_MD show_VB the_DT server_NN working_VBG ._.
    If you're running the server and client on the same machine, then you can omit the -host argument. You can provide other MaxentTagger options to the server invocation of MaxentTaggerServer, such as -outputFormat tsv, as needed.
  8. Why am I running out of memory, in general?

    If you run the tagger without changing how much memory you give to Java, you may run out of memory. This will be evident when the program terminates with an OutOfMemoryError.

    Running from the command line, you need to supply a flag like -mx1g. The number 1g is just an example; if you do not have that much memory available, use less so your computer doesn't start paging. For running a tagger, -mx500m should be plenty; for training a complex tagger, you may need more memory.

    When running from within Eclipse, follow these instructions to increase the memory given to a program being run from inside Eclipse. Increasing the amount of memory given to Eclipse itself won't help.

    Note also that the method tagger.tokenizeText(reader) will tokenize all the text in a reader, and put it in memory. This is okay for reasonable-size files. However, if you have huge files, this can consume an unbounded amount of memory. You will need to adopt an alternate strategy where you only tokenize part of the text at a time (e.g., perhaps a paragraph at a time).

  9. What different output formats are available?

    The output tagged text can be produced in several styles. The tags can be separated from the words by a character, which you can specify (this is the default, with an underscore as the separator), or you can get two tab-separated columns (good for spreadsheets or the Unix cut command), or you can get ouptput in XML. An example of each option appears below:

    $ cat > short.txt
    This is a short sentence.
    So is this.
    $ java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/left3words-wsj-0-18.tagger -textFile short.txt -outputFormat slashTags 2> /dev/null
    This_DT is_VBZ a_DT short_JJ sentence_NN ._.
    So_RB is_VBZ this_DT ._.
    $ java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/left3words-wsj-0-18.tagger -textFile short.txt -outputFormat slashTags -tagSeparator \# 2> /dev/null
    This#DT is#VBZ a#DT short#JJ sentence#NN .#.
    So#RB is#VBZ this#DT .#.
    $ java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/left3words-wsj-0-18.tagger -textFile short.txt -outputFormat tsv 2> /dev/null
    This	DT
    is	VBZ
    a	DT
    short	JJ
    sentence	NN
    .	.
    So	RB
    is	VBZ
    this	DT
    .	.
    $ java -cp stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/left3words-wsj-0-18.tagger -textFile short.txt -outputFormat xml 2> /dev/null
    <sentence id="0">
      <word wid="0" pos="DT">This</word>
      <word wid="1" pos="VBZ">is</word>
      <word wid="2" pos="DT">a</word>
      <word wid="3" pos="JJ">short</word>
      <word wid="4" pos="NN">sentence</word>
      <word wid="5" pos=".">.</word>
    <sentence id="1">
      <word wid="0" pos="RB">So</word>
      <word wid="1" pos="VBZ">is</word>
      <word wid="2" pos="DT">this</word>
      <word wid="3" pos=".">.</word>
  10. Is your tagger slow?

    No! Most people who think that the tagger is slow have made the mistake of running it with the model wsj-0-18-bidirectional-distsim.tagger. That model is fairly slow. Essentially, that model is trying to pull out all stops to maximize tagger accuracy. Speed consequently suffers due to choices like using 4th order bidirectional tag conditioning.

    In applications, we nearly always use the english-left3words-distsim.tagger model, and we suggest you do too. It's nearly as accurate (96.97% accuracy vs. 97.32% on the standard WSJ22-24 test set) and is an order of magnitude faster. Comparing apples-to-apples, the Stanford POS tagger isn't slow. For example, the wsj-0-18-left3words-distsim.tagger model is directly comparable to the quite well known MXPOST tagger by Adwait Ratnaparkhi (both use a second order conditioning model and maximum entropy classifiers; both are trained on about the same amount o data; both are in Java). Compared to MXPOST, the Stanford POS Tagger with this model is both more accurate and considerably faster. Want a number? It all depends, but on a 2008 nothing-special Intel server, it tags about 15000 words per second. This is also about 4 times faster than Tsuruoka's C++ tagger which has an accuracy in between our left3words and bidirectional-distsim models. The LTAG-spinal POS tagger, another recent Java POS tagger, is minutely more accurate than our best model (97.33% accuracy) but it is over 3 times slower than our best model (and hence over 30 times slower than the wsj-0-18-bidirectional-distsim.tagger model).

    However, if speed is your paramount concern, you might want something still faster. This can be done by using a cheaper conditioning model class (you can get another 50% speed up in the Stanford POS tagger, with still little accuracy loss), using some other classifier type (an HMM-based tagger is just going to be faster than a discriminative, feature-based model like our maxent tagger), or doing more code optimization (probably more to be done here, but the current state is not so bad).

    Some people also use the Stanford Parser as just a POS tagger. It's a quite accurate POS tagger, and so this is okay if you don't care about speed. But, if you do, it's not a good idea. Use the Stanford POS tagger.

  11. How do I train a tagger?

    You need to start with a .props file which contains options for the tagger to use. The .props files we used to create the sample taggers are included in the models directory; you can start from whichever one seems closest to the language you want to tag. For example, to train a new English tagger, start with the left3words tagger props file. To train a tagger for a western language other than English, you can consider the props files for the German or the French taggers, which are included in the full distribution. For languages using a different character set, you can start from the Chinese or Arabic props files. Or you can use the -genprops option to MaxentTagger, and it will write a sample properties file, with documentation, for you to modify. It writes it to stdout, so you'll want to save it to some file by redirecting output (usually with >). The # at the start of the line makes things a comment, so you'll want to delete the # before properties you wish to specify.

    In these props files, there are two parameters you absolutely have to change. The first is the model parameter, which specifies the file which the trained model is output to (that is, it is created during the tagger training process). The other is the trainFile parameter, which specifies the file to load the training data from (data that you must provide). So you might have something like:

    model = icelandic.tagger
    trainFile = tagged-icelandic.tsv

    You can specify input files in a few different formats. This is part of the trainFile property. To learn more about the formats you can use and what other the options mean, look at the javadoc for MaxentTagger.

    In its most basic format, the training data is sentences of tagged text. The words should be tagged by having the word and the tag separated by the tagSeparator parameter. For example, if the tagSeparator is _, one of your training lines might look like

    An_DT avocet_NN is_VBZ a_DT small_JJ ,_, cute_JJ bird_NN ._.

    There are other options available for training files. For example, you can use tab separated blocks, where each line represents a word/tag pair and sentences are separated by blank lines. You can also specify PTB-format trees, where the tags are extracted from the bottom layer of the tree.

    If you are training a tagger for a language other than the language used in the properties file, you also need to change the language setting. Certain languages have preset definitions, such as English, Chinese, French, German, and Arabic. For all others, you need to clear the lang field and then set either openClassTags or closedClassTags. Alternatively, you can make code changes to edu.stanford.nlp.tagger.maxent.TTags to implement defaults for your new language.

    You may want to experiment with other feature architectures for your tagger. This is the "arch" property. Look at the javadoc for ExtractorFrames and ExtractorFramesRare to learn what other arch options exist. You might want to start with a basic tagger with the options arch=words(-1,1),unicodeshapes(-1,1),order(2),suffix(4). This will create a tagger with features predicting the current tag from each of the previous, current and next words (words(-1,1)), features from each of those words represented in terms of the unicode character classes they contain (unicodeshapes(-1,1)), bigram and trigram tag sequence features that predict the current tag from the previous one or two tags (order(2)), and additional features for trying to predict the tag of rare or unknown words from the last 1, 2, 3, and 4 characters of the word (suffix(4)). Finally, you need to specify an optimization method with the search property. We build many of our taggers with the owlqn optimizer, but we don't distribute that. Good choices which you can use are the basically equivalent owlqn2 optimizer or qn. (If using qn, set sigmaSquared L2 regularization to a non-zero value, such as 1.0.) You can find the commands for training and testing in the MaxentTagger class javadoc.

  12. What model should I use?

    If you are tagging English, you should almost certainly choose the model english-left3words-distsim.tagger. Included in the distribution is a file, README-Models.txt, which describes all of the available models. For English, there are models trained on WSJ PTB, which are useful for the purposes of academic comparisons. There are also models titled "english" which are trained on WSJ with additional training data, which are more useful for general purpose text. There are models for other languages, as well, such as Chinese, Arabic, etc.

  13. What is the difference between "english" and "wsj"?

    The models with "english" in the name are trained on additional text corresponding to the same data the "english" parser models are trained on, with the exception of instead using WSJ 0-18.

  14. What are the distsim clusters used by the tagger?

    These clusters are a feature extracted from larger, untagged text which clusters the words into similar classes.

    Here are the clusters currently used for English.

  15. How do I tag one pre-tokenized sentence per line?

    Run the tagger with the flags -sentenceDelimiter newline -tokenize false

  16. How do I tag one pre-tokenized sentence per line?

    Run the tagger with the flag -sentenceDelimiter newline

  17. Why does it crash when I try to optimize with search=owlqn? Is owlqn available anywhere?

  18. Unfortunately, we do not have a license to redistribute owlqn. This causes it to crash if you base your training file off a .props file that used owlqn internally. We do distribute our own experimental L1-regularized optimizer, though, which you can use with the option

    or you can use a different optimizer, such as the L2-regularized L-BFGS optimizer

  19. How do I output the results to a file?

  20. Use the shell redirect > filename

  21. How do I fix the Stanford POS Tagger giving a NoSuchMethodError or NoSuchFieldError?

    If you see an Exception stacktrace message like:

    Exception in thread "main" java.lang.NoSuchFieldError: featureFactoryArgs


    Exception in thread "main" java.lang.NoSuchMethodError: edu.stanford.nlp.tagger.maxent.TaggerConfig.getTaggerDataInputStream(Ljava/lang/String;)Ljava/io/DataInputStream;


    Caused by: java.lang.NoSuchMethodError: edu.stanford.nlp.util.Generics.newHashMap()Ljava/util/Map;
        at edu.stanford.nlp.pipeline.AnnotatorPool.(
        at edu.stanford.nlp.pipeline.StanfordCoreNLP.getDefaultAnnotatorPool(

    then this isn't caused by the shiny new Stanford NLP tools that you've just downloaded. It is because you also have old versions of one or more Stanford NLP tools on your classpath.

    The straightforward case is if you have an older version of a Stanford NLP tool. For example, you may still have a version of Stanford NER on your classpath that was released in 2009. In this case, you should upgrade, or at least use matching versions. For any releases from 2011 on, just use tools released at the same time -- such as the most recent version of everything :) -- and they will all be compatible and play nicely together.

    The tricky case of this is when people distribute jar files that hide other people's classes inside them. People think this will make it easy for users, since they can distribute one jar that has everything you need, but, in practice, as soon as people are building applications using multiple components, this results in a particular bad form of jar hell. People just shouldn't do this. The only way to check that other jar files do not contain conflicting versions of Stanford tools is to look at what is inside them (for example, with the jar -tf command).

    In practice, if you're having problems, the most common cause (in 2013-2014) is that you have ark-tweet-nlp on your classpath. The jar file in their github download hides old versions of many other people's jar files, including Apache commons-codec (v1.4), commons-lang, commons-math, commons-io, Lucene; Twitter commons; Google Guava (v10); Jackson; Berkeley NLP code; Percy Liang's fig; GNU trove; and an outdated version of the Stanford POS tagger (from 2011). You should complain to them for creating you and us grief. But you can then fix the problem by using their jar file from Maven Central. It doesn't have all those other libraries stuffed inside.

You can discuss other topics with Stanford POS Tagger developers and users by joining the java-nlp-user mailing list (via a webpage). Or you can send other questions and feedback to