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This is a new backend implementation of the ANNIS linguistic search and visualization system.
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README.md

graphANNIS DOI

Documentation Build Status
Developer Guide Build Status (Linux & MacOS)
API documentation (API) Build status (Windows)

This is a new backend implementation of the ANNIS linguistic search and visualization system (http://corpus-tools.org/annis/).

While there are test cases, this project is still in a pre-release beta phase. Only a sub-set of the ANNIS Query Langugage (AQL) is supported yet (full support is planned). Integration with ANNIS is currently implemented in a special branch. There is a tutorial in the Developer Guide on how to embedd graphANNIS in your own application.

The basic design ideas and data models are described in detail in the PhD-thesis "ANNIS: A graph-based query system for deeply annotated text corpora". The thesis describes a prototype implementation in C++ and not Rust, but the design ideas are the same. Noteable differences/enhancements compared to the thesis are:

  • Graph storages implement querying inverse edges and finding reachable nodes based on them: this allows to implement inverse operators (e.g. for precedence) and switching operands in situations where it was not possible before.
  • The data model has been simplified: the inverse coverage component and inverse edges in the left-/right-most token component have been removed.
  • Additional query language features are now supported.

How to compile

graphANNIS is written in the Rust programming language (https://www.rust-lang.org). You can install Rust from https://www.rust-lang.org/tools/install. After you have installed Rust, you can can build the complete project with

cargo build --release --all-features

3rd party dependencies

This software depends on several 3rd party libraries. These are documented in the BOM.txt file in this folder.

Language bindings

Author(s)

How to cite

@phdthesis{Krause2019ANNIS:,
author = {Krause, Thomas},
title = {ANNIS: A graph-based query system for deeply annotated text corpora},
school = {Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät},
year = {2019},
doi = {http://dx.doi.org/10.18452/19659}
}
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