This framework provides an easy and accurate method to annotate monotonicity information (polarity arrows) on natural English sentences based on Universal Dependency parse trees.
The following publications are integrated in this framework:
The recoomanded environment include Python 3.6 or higher , Stanza v1.2.0 or higher, and **ImageMagick v7.0.11. The code does not work with Python 2.7.
Clone the repository
git clone https://github.com/eric11eca/Udep2Mono.git
Install from sources
pip install -r requirements.txt
python -m pip install --upgrade setuptools
python setup.py install
First download a pretrained model from Google Drive. Replace the Stanza defalut depparse model with this pretrained version. The Stanza model path is:
C:\Users\$your_user_name$\stanza_resources\en\
Then either open Udep2Mono.ipynb (recommanded) or run
python main.py
We provide two UD Parser Models for English. Some models are general purpose models, while others produce embeddings for specific use cases. Pre-trained models can be loaded by just passing the model name: SentenceTransformer('model_name').
For training new UD parser models, see Stanza's training dcumentation for an introduction how to train your own UD parser.
If you find this repository helpful, feel free to cite our publication Monotonicity Marking from Universal Dependency Trees:
@InProceedings{chen-gao:2021:IWCS,
author = {Chen, Zeming and Gao, Qiyue},
title = {Monotonicity Marking from Universal Dependency Trees},
booktitle = {Proceedings of the 14th International Conference on Computational Semantics (IWCS)},
month = {June},
year = {2021},
address = {Groningen, The Netherlands (online)},
publisher = {Association for Computational Linguistics},
pages = {121--131},
url = {https://www.aclweb.org/anthology/2021.iwcs-1.12}
}Contact person: Zeming Chen, chenz16@rose-hulman.edu Don't hesitate to send us an e-mail or report an issue, if something is broken or if you have further questions.
This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.