Stage-oe-small.jpg

Inproceedings3942

Aus Aifbportal
Version vom 4. Mai 2022, 14:33 Uhr von Jg2894 (Diskussion | Beiträge)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche


AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities


AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities



Published: 2022

Buchtitel: Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Verlag: Association for Computational Linguistics

Referierte Veröffentlichung

BibTeX

Kurzfassung
In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in sequence, our system incorporates information from relation extraction into entity extraction. This means that the system can be trained even on data sets where only a subset of all valid entity spans is annotated. We provide an extensive evaluation of the proposed system and its strengths and weaknesses. Our approach, which can be scaled dynamically in computational complexity at inference time, produces predictions with high precision and reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the domain of physics and math, it achieves high relation extraction macro F1 scores of 95.43% and 79.17%, respectively. The code used for training and evaluating our models is available at: https://github.com/nicpopovic/RE1st

Download: Media:Semeval2022_RE1st.pdf



Forschungsgruppe

Web Science


Forschungsgebiet

Text Mining, Informationsextraktion, Natürliche Sprachverarbeitung, Deep Learning