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A tokenizer and sentence splitter for German and English web and social media texts.

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SoMaJo is a state-of-the-art tokenizer and sentence splitter for German and English web and social media texts. It won the EmpiriST 2015 shared task on automatic linguistic annotation of computer-mediated communication / social media. As such, it is particularly well-suited to perform tokenization on all kinds of written discourse, for example chats, forums, wiki talk pages, tweets, blog comments, social networks, SMS and WhatsApp dialogues.

In addition to tokenizing the input text, SoMaJo can also output token class information for each token, i.e. if it is a number, an emoticon, an abbreviation, etc.:

echo 'Wow, superTool!;)' | somajo-tokenizer -c -t -
Wow	regular
,	symbol
super	regular
Tool	regular
!	symbol
;)	emoticon

SoMaJo can also output additional information for each token that can help to reconstruct the original untokenized text (to a certain extent):

echo 'der beste Betreuer? - >ProfSmith! : )' | somajo-tokenizer -c -e -
Betreuer	SpaceAfter=No
->	SpaceAfter=No, OriginalSpelling="- >"
Prof	SpaceAfter=No
Smith	SpaceAfter=No
:)	OriginalSpelling=": )"

The -t and -e options can also be used in combination, of course.

SoMaJo can split the input text into sentences using the --split_sentences option.

SoMaJo has full XML support, i.e. it can perform sensible tokenization and sentence splitting on well-formed XML files using the --xml and --tag options.

The system is described in greater detail in Proisl and Uhrig (2016).

For part-of-speech tagging, we recommend SoMeWeTa, a part-of-speech tagger with state-of-the-art performance on German web and social media texts:

somajo-tokenizer --split_sentences <file> | somewe-tagger --tag <model> -


SoMaJo can be easily installed using pip (pip3 in some distributions):

pip install -U SoMaJo

Alternatively, you can download and decompress the latest release or clone the git repository:

git clone

In the new directory, run the following command:

pip install -U .


Using the somajo-tokenizer executable

You can use the tokenizer as a standalone program from the command line. General usage information is available via the -h option:

somajo-tokenizer -h

To tokenize a text file according to the guidelines of the EmpiriST 2015 shared task, just call the tokenizer like this:

somajo-tokenizer -c <file>

If you do not want to split camel-cased tokens, simply drop the -c option:

somajo-tokenizer <file>

The tokenizer can also output token class information for each token, i.e. if it is a number, an emoticon, an abbreviation, etc.:

somajo-tokenizer -t <file>

If you want to be able to reconstruct the untokenized input to a certain extent, SoMaJo can also provide you with additional details for each token, i.e. if the token was followed by whitespace or if it contained internal whitespace (according to the EmpiriST tokenization guidelines, things like “: )” get normalized to “:)”):

somajo-tokenizer -e <file>

SoMaJo assumes that paragraphs are delimited by empty lines in the input file. If your input file uses single newlines instead, you have to tell that to the tokenizer via the -s or --paragraph_separator option:

somajo-tokenizer --paragraph_separator single_newlines <file>

To speed up tokenization, you can specify the number of worker processes used via the --parallel option:

somajo-tokenizer --parallel <number> <file>

SoMaJo can split the input paragraphs into sentences:

somajo-tokenizer --split_sentences <file>

SoMaJo can also process XML files. Use the -x or --xml option to tell the tokenizer that your input is an XML file:

somajo-tokenizer --xml <xml-file>

If you also want to do sentence splitting, you can use (multiple instances of) the --tag option to specify XML tags that are always sentence breaks, i.e. that can never occur in the middle of a sentence. Per default, the sentence splitter uses the following list of tags: title, h1, h2, h3, h4, h5, h6, p, br, hr, div, ol, ul, dl and table.

somajo-tokenizer --xml --split_sentences --tag h1 --tag p --tag div <xml-file>

Using the module

You can easily incorporate SoMaJo into your own Python projects. All you need to do is importing somajo.SoMaJo, creating a SoMaJo object and calling one of its tokenizer functions: tokenize_text, tokenize_text_file, tokenize_xml or tokenize_xml_file. These functions return a generator that yields tokenized chunks of text. By default, these chunks of text are sentences. If you set split_sentences=False, then the chunks of text are either paragraphs or chunks of XML. Every tokenized chunk of text is a list of Token objects.

For more details, take a look at the API documentation.

Here is an example for tokenizing and sentence splitting two paragraphs:

from somajo import SoMaJo

tokenizer = SoMaJo("de_CMC", split_camel_case=True)

# note that paragraphs are allowed to contain newlines
paragraphs = ["der beste Betreuer?\n-- ProfSmith! : )",
              "Was machst du morgen Abend?! Lust auf Film?;-)"]

sentences = tokenizer.tokenize_text(paragraphs)
for sentence in sentences:
    for token in sentence:
        print("{}\t{}\t{}".format(token.text, token.token_class, token.extra_info))

And here is an example for tokenizing and sentence splitting a whole file. The option paragraph_separator="single_newlines" states that paragraphs are delimited by newlines instead of empty lines:

sentences = tokenizer.tokenize_text_file("Beispieldatei.txt", paragraph_separator="single_newlines")
for sentence in sentences:
    for token in sentence:

For processing XML data, use the tokenize_xml or tokenize_xml_file methods:

eos_tags = ["title", "h1", "p"]

# you can read from an open file object
sentences = tokenizer.tokenize_xml_file(file_object, eos_tags)
# or you can specify a file name
sentences = tokenizer.tokenize_xml_file("Beispieldatei.xml", eos_tags)
# or you can pass a string with XML data
sentences = tokenizer.tokenize_xml(xml_string, eos_tags)

for sentence in sentences:
    for token in sentence:


SoMaJo was the system with the highest average F₁ score in the EmpiriST 2015 shared task. The performance of the current version on the two test sets is summarized in the following table (Training and test sets are available from the official website):

Corpus Precision Recall F₁
CMC 99.71 99.56 99.64
Web 99.91 99.92 99.91

Tokenizing English text

Starting with version 1.8.0, SoMaJo can also tokenize English text. In general, we follow the “new” Penn Treebank conventions described, for example, in the guidelines for ETTB 2.0 (Mott et al., 2009) and CLEAR (Warner et al., 2012).

For tokenizing English text on the command line, specify the language via the -l or --language option:

somajo-tokenizer -l en_PTB <file>

From Python, you can pass language="en_PTB" to the SoMaJo constructor, e.g.:

paragraphs = ["That aint bad!:D"]
tokenizer = SoMaJo(language="en_PTB")
sentences = tokenizer.tokenize_text(paragraphs)

Performance of the English tokenizer:

Corpus Precision Recall F₁
English Web Treebank 99.66 99.64 99.65


Here are some brief notes to help you get started:

  • Preferably create a dedicated virtual environment.

  • Make sure you have pip ≥ 21.3.

  • Install the project in editable mode:

    pip install -U -e .
  • Install the development dependencies:

    pip install -r requirements_dev.txt
  • To run the tests:

    python3 -m unittest discover
  • To build the documentation:

    cd doc
    make markdown

    Note that the created markdown is not perfect and needs some manual postprocessing.

  • To build the distribution files:

    python3 -m build


  • Proisl, Thomas, and Peter Uhrig. 2016. “SoMaJo: State-of-the-Art Tokenization for German Web and Social Media Texts.” In Proceedings of the 10th Web as Corpus Workshop (WAC-X) and the EmpiriST Shared Task, edited by Paul Cook, Stefan Evert, Roland Schäfer, and Egon Stemle, 57–62. Berlin: Association for Computational Linguistics.

      author    = {Proisl, Thomas and Uhrig, Peter},
      title     = {{SoMaJo}: {S}tate-of-the-art tokenization for {G}erman web and social media texts},
      year      = {2016},
      booktitle = {Proceedings of the 10th {W}eb as {C}orpus Workshop ({WAC-X}) and the {EmpiriST} Shared Task},
      editor    = {Cook, Paul and Evert, Stefan and Schäfer, Roland and Stemle, Egon},
      address   = {Berlin},
      publisher = {Association for Computational Linguistics},
      pages     = {57--62},
      doi       = {10.18653/v1/W16-2607},
      url       = {},

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