SoMaJo 1.0.0

A tokenizer for German web and social media texts.

Introduction

SoMaJo is a state-of-the-art tokenizer for German web and social media texts that 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.

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

Installation

SoMaJo can be easily installed using pip:

pip install SoMaJo

Usage

Using the tokenizer executable

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

tokenizer -h

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

tokenizer -c <file>

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

tokenizer <file>

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

tokenizer -t <file>

Using the module

You can easily incorporate the tokenizer into your own Python projects. All you have to do is import somajo.Tokenizer, create a Tokenizer object and call its tokenize method:

from somajo import Tokenizer

tokenizer = Tokenizer(split_camel_case=True, token_classes=False)
tokens = tokenizer.tokenize(paragraph)

Evaluation

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[1]:

Corpus	Precision	Recall	F₁
CMC	99.62	99.56	99.59
Web	99.83	99.92	99.87

[1]

Training and test sets are available from the official website.

References

• Proisl, Thomas, 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). ACL.