TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 30 Dec 2022.
Builds on
7 of 9 references.
References · 7
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 322
- 2017SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publicationscited by 102
- 2017Multi-Task Learning of Keyphrase Boundary Classificationcited by 8
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