APRCOIE means Auto Patterns Recognition for Chinese Open Information Extraction, which is a open information extraction model for Chinese text.
APRCOIE achieves the best performance on the latest Open Information Extraction benchmark BenchIE [1].
This repository contains the code and annatated dataset of APRCOIE model.
The train.py file in the usage folder demonstrates how to conduct training. If you do not have additional annotated data, simply run train.py to complete the training process.
You will get two model files in the data folder: model.obj, model_att.obj.
If you have your own annotated data, it's important to ensure that the data adheres to the following format: : words, dependency, POS, triples which represent annotation result in number.
An example is provided below:
sentence: 美国有50个州。
words = ["美国", "有", "50", "个", "州", "。"]
dependency = [(1, 2, 'SBV'), (2, 0, 'HED'), (3, 4, 'ATT'), (4, 5, 'ATT'), (5, 2, 'VOB'), (6, 2, 'WP')]
POS = ['ns', 'v', 'm', 'q', 'n', 'wp'],
triples_in_number = [[[0], [1], [2, 3, 4]]] (represents the only triple: [[['美国'], ['有'], ['50', '个', '州']]])
Then these data organized as a dictionary as shown in the line 20 in train.py.
The file predict.py in usage folder shows how to extract triples. For a target sentence, it firstly conducts word segmentation, POS tagging, dependency parsing. We adopt LTP 4 [2] as the natural language processing tool here. Then it constructs an instance of class Tree, named test_tree, of the target sentence, loads the model, and passes all of these to extraction_regular and extraction_att functions of MATRIX module.
Jialin Hua (华佳林), nhma0004@gmail.com
[1] K. Gashteovski, M. Yu, B. Kotnis, C. Lawrence, M. Niepert, G. Glavas, BenchIE: A framework for multi-faceted fact-based open information extraction evaluation, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022, pp. 4472-4490.
[2] W. Che, Y. Feng, L. Qin, T. Liu, N-LTP: An Open-source Neural Language Technology Platform for Chinese, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2021, pp. 42-49.