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https://github.com/proycon/colibri-core

Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.
https://github.com/proycon/colibri-core

c-plus-plus computational-linguistics corpus library linguistics ngram ngrams nlp pattern-recognition python skipgram text-processing

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Colibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.

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README 

        
# Colibri Core



[![GitHub C++ build](https://github.com/proycon/colibri-core/actions/workflows/colibri-core.yml/badge.svg?branch=master)](https://github.com/proycon/colibri-core/actions/)

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*by Maarten van Gompel, [email protected], Radboud University Nijmegen*



*Licensed under GPLv3 (See http://www.gnu.org/licenses/gpl-3.0.html)*



Colibri Core is software to quickly and efficiently count and extract patterns

from large corpus data, to extract various statistics on the extracted

patterns, and to compute relations between the extracted patterns. The employed

notion of pattern or construction encompasses the following categories:



* **n-gram** -- *n* consecutive words

* **skipgram** -- An abstract pattern of predetermined length with one or multiple gaps (of specific size).

* **flexgram** -- An abstract pattern with one or more gaps of variable-size.



N-gram extraction may seem fairly trivial at first, with a few lines in your

favourite scripting language, you can move a simple sliding window of size **n**

over your corpus and store the results in some kind of hashmap. This trivial

approach however makes an unnecessarily high demand on memory resources, this

often becomes prohibitive if unleashed on large corpora. Colibri Core tries to

minimise these space requirements in several ways:



* **Compressed binary representation** -- Each word type is assigned a numeric class, which is encoded in a compact binary format in which highly frequent classes take less space than less frequent classes. Colibri core always uses this representation rather than a full string representation, both on disk and in memory.

* **Informed iterative counting** -- Counting is performed more intelligently by iteratively processing the corpus in several passes and quickly discarding patterns that won't reach the desired occurrence threshold.



Skipgram and flexgram extraction are computationally more demanding but have

been implemented with similar optimisations. Skipgrams are computed by

abstracting over n-grams, and flexgrams in turn are computed either by

abstracting over skipgrams, or directly from n-grams on the basis of

co-occurrence information (mutual pointwise information).



At the heart of the sofware is the notion of pattern models. The core tool, to

be used from the command-line, is ``colibri-patternmodeller`` which enables you

to build pattern models, generate statistical reports, query for specific

patterns and relations, and manipulate models.



A pattern model is simply a collection of extracted patterns (any of the three

categories) and their counts from a specific corpus. Pattern models come in two

varieties:



* **Unindexed Pattern Model** -- The simplest form, which simply stores the patterns and their count.

* **Indexed Pattern Model** -- The more informed form, which retains all indices to the original corpus, at the cost of more memory/diskspace.



The Indexed Pattern Model is much more powerful, and allows more statistics and

relations to be inferred.



The generation of pattern models is optionally parametrised by a minimum

occurrence threshold, a maximum pattern length, and a lower-boundary on the

different types that may instantiate a skipgram (i.e. possible fillings of the

gaps).



## Technical Details



Colibri Core is available as a collection of **standalone command-line tools**,

as a **C++ library**, and as a **Python library**.



Please consult the full documentation at 



## Installation



### Python binding



For the Colibri Core Python library, just install using:



```

pip install colibricore

```



We strongly recommend you use a Virtual Environment for this. Do note that this

is only available for unix-like systems, Windows is not supported.



### Installation from packages



For the command-line tools, check if your distribution has a package available.

There are packages for Alpine Linux (`apk add colibri-core`) and for macOS with

homebrew (`brew tap fbkarsdorp/homebrew-lamachine && brew install

colibri-core`). Note that these do not contain the Python binding!



Otherwise you will need to either use the container image or to build and install from source.



### Installation from source



If no packages are available, you will need to compile from source or use the container build (e.g.

Docker) as explained later on.



In order to do so, you need a sane build environment, install the necessary dependencies for your distribution:



For Debian/Ubuntu::



```

$ sudo apt-get install make gcc g++ pkg-config autoconf-archive libtool autotools-dev libbz2-dev zlib1g-dev libtar-dev python3 python3-dev cython3

```



For RedHat-based systems (run as root)::



```

# yum install pkgconfig libtool autoconf automake autoconf-archive make gcc gcc-c++ libtar libtar-devel python3 python3-devel zlib zlib-devel python3-pip bzip2 bzip2-devel cython3

```



For macOS with homebrew:



```

$ brew install autoconf automake libtool autoconf-archive python3 pkg-config

```



Then clone this repository and install as follows:



```

$ bash bootstrap

$ ./configure

$ make

$ sudo make install

```



### Container usage



The Colibri Core command-line tools are also available as an OCI/Docker container.



A pre-made container image can be obtained from Docker Hub as follows:



``docker pull proycon/colibri-core``



You can also build a container image yourself as follows, make sure you are in the root of this repository:



``docker build -t proycon/colibri-core .``



This builds the latest stable release, if you want to use the latest development version

from the git repository instead, do:



``docker build -t proycon/colibri-core --build-arg VERSION=development .``



Run the frog container interactively as follows, it will dump you into a shell where the various command line tools are available:



``docker run -t -i proycon/colibri-core``



Add the ``-v /path/to/your/data:/data`` parameter if you want to mount your data volume into the container at `/data`.



## Demo



![Colibri Core Demo](https://raw.githubusercontent.com/CLARIAH/wp3-demos/master/colibri-core.gif)



## Publication



This software is extensively described in the following peer-reviewed publication:



    van Gompel, M and van den Bosch, A (2016)

    Efficient n-gram, Skipgram and Flexgram Modelling with Colibri Core.

    *Journal of Open Research Software*

    4: e30, DOI: http://dx.doi.org/10.5334/jors.105



Access the publication [here](http://dx.doi.org/10.5334/jors.105) and please cite it if you make use of

Colibri Core in your work.