@inproceedings{a23e4214cda94989ab3dd77b490b323f,
title = "Enhancing joint entity and relation extraction with language modeling and hierarchical attention",
abstract = "Both entity recognition and relation extraction can benefit from being performed jointly, allowing them to enhance each other. However, existing methods suffer from the sparsity of relevant labels and strongly rely on external natural language processing tools, leading to error propagation. To tackle these problems, we propose an end-to-end joint framework for entity recognition and relation extraction with an auxiliary training objective on language modeling, i.e., learning to predict surrounding words for each word in sentences. Furthermore, we incorporate hierarchical multi-head attention mechanisms into the joint extraction model to capture vital semantic information from the available texts. Experiments show that the proposed approach consistently achieves significant improvements on joint extraction task of entities and relations as compared with strong baselines.",
keywords = "Entity recognition, Hierarchical attention, Joint model, Language modeling objective, Relation extraction",
author = "Renjun Chi and Bin Wu and Linmei Hu and Yunlei Zhang",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 ; Conference date: 01-08-2019 Through 03-08-2019",
year = "2019",
doi = "10.1007/978-3-030-26072-9_24",
language = "English",
isbn = "9783030260712",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "314--328",
editor = "Jie Shao and Yiu, {Man Lung} and Masashi Toyoda and Dongxiang Zhang and Wei Wang and Bin Cui",
booktitle = "Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings",
address = "Germany",
}
