AAAI 2023technical1 citations
TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)
Răzvan-Alexandru Smădu, George-Eduard Zaharia, Andrei-Marius Avram, Dumitru-Clementin Cercel, Mihai Dascalu, Florin Pop
Abstract
Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.
BibTeX
@article{Smădu_Zaharia_Avram_Cercel_Dascalu_Pop_2024, title={TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27027}, DOI={10.1609/aaai.v37i13.27027}, abstractNote={Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Smădu, Răzvan-Alexandru and Zaharia, George-Eduard and Avram, Andrei-Marius and Cercel, Dumitru-Clementin and Dascalu, Mihai and Pop, Florin}, year={2024}, month={Jul.}, pages={16334-16335} }