Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition

Avishek Lahiri, Pratyay Sarkar, Medha Sen, Debarshi Sanyal, Imon Mukherjee


Abstract
Scientific texts are distinctive from ordinary texts in quite a few aspects like their vocabulary and discourse structure. Consequently, Information Extraction (IE) tasks for scientific texts come with their own set of challenges. The classical definition of Named Entities restricts the inclusion of all scientific terms under its hood, which is why previous works have used the terms Named Entities and Keyphrases interchangeably. We suggest the rechristening of Named Entities for the scientific domain as Typed Keyphrases (TK), broadening their scope. We advocate for exploring this task in the few-shot domain due to the scarcity of labeled scientific IE data. Currently, no dataset exists for few-shot scientific Typed Keyphrase Recognition. To address this gap, we develop an annotation schema and present Few-TK, a dataset in the AI/ML field that includes scientific Typed Keyphrase annotations on abstracts of 500 research papers. To the best of our knowledge, this is the introductory few-shot Typed Keyphrase recognition dataset and only the second dataset structured specifically for few-shot NER, after Few-NERD. We report the results of several few-shot sequence-labelling models applied to our dataset. The data and code are available at https://github.com/AvishekLahiri/Few_TK.git
Anthology ID:
2024.findings-naacl.253
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
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Publisher:
Association for Computational Linguistics
Note:
Pages:
4011–4025
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URL:
https://aclanthology.org/2024.findings-naacl.253
DOI:
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Cite (ACL):
Avishek Lahiri, Pratyay Sarkar, Medha Sen, Debarshi Sanyal, and Imon Mukherjee. 2024. Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4011–4025, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Few-TK: A Dataset for Few-shot Scientific Typed Keyphrase Recognition (Lahiri et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.253.pdf
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 2024.findings-naacl.253.copyright.pdf