TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)

Authors

  • Răzvan-Alexandru Smădu Faculty of Automatic Control and Computers, University Politehnica of Bucharest
  • George-Eduard Zaharia Faculty of Automatic Control and Computers, University Politehnica of Bucharest
  • Andrei-Marius Avram Faculty of Automatic Control and Computers, University Politehnica of Bucharest
  • Dumitru-Clementin Cercel Faculty of Automatic Control and Computers, University Politehnica of Bucharest
  • Mihai Dascalu Faculty of Automatic Control and Computers, University Politehnica of Bucharest
  • Florin Pop Faculty of Automatic Control and Computers, University Politehnica of Bucharest National Institute for Research and Development in Informatics - ICI Bucharest, Romania

DOI:

https://doi.org/10.1609/aaai.v37i13.27027

Keywords:

Domain Adaptation, Keyphrase Extraction, Adversarial Examples

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.
AAAI-23 Proceedings Cover

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Published

2024-07-15

How to Cite

Smădu, R.-A., Zaharia, G.-E., Avram, A.-M., Cercel, D.-C., Dascalu, M., & Pop, F. (2024). TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16334-16335. https://doi.org/10.1609/aaai.v37i13.27027

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program