Warning: sticker is succeeded by
SyntaxDot, which supports
many new features:
- Multi-task learning.
- Pretrained transformer models, suchs as BERT and XLM-R.
- Biaffine parsing in addition to parsing as sequence labeling.
- Lemmatization.
sticker
sticker is a sequence labeler using neural networks.
Introduction
sticker is a sequence labeler that uses either recurrent neural
networks, transformers, or dilated convolution networks. In principle,
it can be used to perform any sequence labeling task, but so far the
focus has been on:
- Part-of-speech tagging
- Topological field tagging
- Dependency parsing
- Named entity recognition
Features
- Input representations:
- finalfusion embeddings with subword units
- Bidirectional byte LSTMs
- Hidden representations:
- Bidirectional recurrent neural networks (LSTM or GRU)
- Transformers
- Dillated convolutions
- Classification layers:
- Deployment:
- Standalone binary that links against
libtensorflow
- Very liberal license
- Docker containers with models
Status
sticker is almost production-ready and we are preparing for release
1.0.0. Graphs and models crated with the current version must work
with sticker 1.x.y. There may still be breaking API or configuration
file changes until 1.0.0 is released.
Where to go from here
References
sticker uses techniques from or was inspired by the following papers:
- Finding Function in Form: Compositional Character Models for Open
Vocabulary Word
Representation. Wang
Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez,
Silvio Amir, Luís Marujo, Tiago Luís, 2015, Proceedings of the 2015
Conference on Empirical Methods in Natural Language Processing
- Transition-based dependency parsing with topological
fields. Daniël
de Kok, Erhard Hinrichs, 2016, Proceedings of the 54th Annual
Meeting of the Association for Computational Linguistics
- Viable Dependency Parsing as Sequence
Labeling. Michalina
Strzyz, David Vilares, Carlos Gómez-Rodríguez, 2019, Proceedings of
the 2019 Conference of the North American Chapter of the Association
for Computational Linguistics: Human Language Technologies
Issues
You can report bugs and feature requests in the sticker issue
tracker.
License
sticker is licensed under the Blue Oak Model License version
1.0.0. The Tensorflow protocol buffer definitions in
tf-proto
are licensed under the Apache License version 2.0. The
list of contributors is also available.
Credits
- sticker is developed by Daniël de Kok & Tobias Pütz.
- The Python precursor to sticker was developer by Erik Schill.
- Sebastian Pütz and Patricia Fischer reviewed a lot of code across
the sticker projects.