Using Chunks


LanguageTool has a so-called chunker for English. It detects chunks like noun chunks or verb chunks (also known as noun phrases and verb phrases). Consider, for example, this sentence:

She put the big knives on the table.

It contains three noun phrases, printed in bold:

She put the big knives on the table.

Its analysis will look like this:


Chunk Tags

A chunk tag like B-NP-singular is made up of two or three parts:

First part:

As a chunk may be only one word long (like “She” in the example above), it can be both beginning and end of a chunk at the same time.

Second part:

Third part, only for noun chunks (this is an extension by LanguageTool over the original chunks):

To see how a text is chunked use the -v parameter of the command line tool.

Chunks in Pattern Rules

Chunks can be used to match text in order to find mistakes using the chunk attribute in the grammar.xml file. This will match any word that has the B-NP-singular chunk tag:

<token chunk="B-NP-singular"/>

The matching of chunks can be combined with other ways to match text. The next example will match the end of a noun chunk, but only if it is not the word will:

<token chunk="E-NP-singular"><exception>will</exception></token>

To specify a chunk as a regular expression, use:

<token chunk_re="[IB]-VP">

Note that there is currently no way to negate chunks.


We use the chunker from OpenNLP. As a chunker needs disambiguated input (e.g. it must be clear whether “walk” is a noun or a verb in the given context), we’re also using the OpenNLP part-of-speech tagger (POS tagger) for chunking. Using our own POS tagger isn’t feasible, as its results are ambiguous unless disambiguated by our disambuation.xml. The OpenNLP POS tagger has been trained on text tokenized with the OpenNLP tokenizer, so we also have to use that instead of our own (but only for chunking).

Here’s an overview of the steps that we run to get chunked text:

  1. Tokenizer (in
  2. POS Tagger (in
  3. Chunker (in
  4. our own filter (in - this adds singular/plural information

OpenNLP has been trained on tagged text and uses statistics to get the most probable result. Thus OpenNLP will not be 100% correct all the time.