commit	eed234f61e0bd0f672f0156b8b6759128c917f2d	[log] [tgz]
author	dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	Tue Jan 03 18:01:19 2023 +0000
committer	Marc Kupietz <kupietz@ids-mannheim.de>	Wed Mar 01 21:54:31 2023 +0100
tree	a42c43c605f7a79506967a8c94bfd5864cbb9778
parent	56403bba15325a8a306bae3af6b8f2b2c1162262 [diff]

tree: a42c43c605f7a79506967a8c94bfd5864cbb9778

Readme.md

KorAP Tokenizer

Interface and implementation of a tokenizer and sentence splitter that can be used

for German, English, French, and with some limitations also for other languages
as standalone tokenizer and/or sentence splitter
or within the KorAP ingestion pipeline
or within the OpenNLP tools framework

The included implementations (DerekoDfaTokenizer_de, DerekoDfaTokenizer_en, DerekoDfaTokenizer_fr) are highly efficient DFA tokenizers and sentence splitters with character offset output based on JFlex. The de-variant is used for the German Reference Corpus DeReKo. Being based on finite state automata, the tokenizers are potentially not as accurate as language model based ones, but with ~5 billion words per hour typically more efficient. An important feature in the DeReKo/KorAP context is also that token character offsets can be reported, which can be used for applying standoff annotations.

The include mplementations of the KorapTokenizer interface also implement the opennlp.tools.tokenize.Tokenizer and opennlp.tools.sentdetect.SentenceDetector interfaces and can thus be used as a drop-in replacements in OpenNLP applications.

The underlying scanner is based on the Lucene scanner with modifications from David Hall.

Our changes mainly concern a good coverage of German, or optionally of some English and French abbreviations, and some updates for handling computer mediated communication, optimized and tested, in the case of German, against the gold data from the EmpiriST 2015 shared task (Beißwenger et al. 2016).

Installation

mvn clean install

Note

Because of the large table of abbreviations, the conversion from the jflex source to java, i.e. the calculation of the DFA, takes about 5 to 30 minutes, depending on your hardware, and requires a lot of heap space.

Examples Usage

By default, KorAP tokenizer reads from standard input and writes to standard output. It supports multiple modes of operations.

Split English text into tokens

$ echo "It's working." | java -jar target/KorAP-Tokenizer-2.2.2-standalone.jar -l en
It
's
working
.

Split French text into tokens and sentences

$ echo "C'est une phrase. Ici, il s'agit d'une deuxième phrase." \
  | java -jar target/KorAP-Tokenizer-2.2.2-standalone.jar -s -l fr
C'
est
une
phrase
.

Ici
,
il
s'
agit
d'
une
deuxième
phrase
.

Print token character offsets

With the --positions option, for example, the tokenizer prints all offsets of the first character of a token and the first character after a token. In order to end a text, flush the output and reset the character position, an EOT character (0x04) can be used.

$ echo -n -e 'This is a text.\x0a\x04\x0aAnd this is another text.\n\x04\n' |\
     java -jar target/KorAP-Tokenizer-2.2.2-standalone.jar  --positions
This
is
a
text
.
0 4 5 7 8 9 10 14 14 15
And
this
is
another
text
.
0 3 4 8 9 11 12 19 20 24 24 25

Print token and sentence offset

echo -n -e ' This ist a start of a text. And this is a sentence!!! But what the hack????\x0a\x04\x0aAnd this is another text.'  |\
   java -jar target/KorAP-Tokenizer-2.2.2-standalone.jar --no-tokens --positions --sentence-boundaries
1 5 6 9 10 11 12 17 18 20 21 22 23 27 27 28 29 32 33 37 38 40 41 42 43 51 51 54 55 58 59 63 64 67 68 72 72 76
1 28 29 54 55 76
0 3 4 8 9 11 12 19 20 24 24 25
0 25

Adding Support for more Languages

To adapt the included implementations to more languages, take one of the language-specific_<language>.jflex-macro files as template and modify for example the macro for abbreviations SEABBR. Then add an execution section for the new language to the jcp (java-comment-preprocessor) artifact in pom.xml following the example of one of the configurations there. After building the project (see below) your added language specific tokenizer / sentence splitter should be selectable with the --language option.

Alternatively, you can also provide KorAPTokenizer implementations independently on the class path and select them with the --tokenizer-class option.

Development and License

Authors:

This package is developed as part of the KorAP Corpus Analysis Platform at the Leibniz Institute for German Language (IDS).

The package contains code from Apache Lucene with modifications by Jim Hall.

It is published under the Apache 2.0 License.

Contributions

Contributions are very welcome!

Your contributions should ideally be committed via our Gerrit server to facilitate reviewing (see Gerrit Code Review - A Quick Introduction if you are not familiar with Gerrit). However, we are also happy to accept comments and pull requests via GitHub.

References

Beißwenger, Michael / Bartsch, Sabine / Evert, Stefan / Würzner, Kay-Michael (2016): EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora. 44-56. 10.18653/v1/W16-2606.
Diewald, Nils / Kupietz, Marc / Lüngen, Harald (2022): Tokenizing on scale – Preprocessing large text corpora on the lexical and sentence level. In Klosa-Kückelhaus, Annette / Engelberg, Stefan / Möhrs, Christine / Storjohann, Petra (eds.): Proceedings of the XX EURALEX International Congress (EURALEX 2022).