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license: cc-by-4.0
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---
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---
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license: cc-by-4.0
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task_categories:
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- text-classification
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language:
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- en
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tags:
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- NLP
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- LLM
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- hierarchical
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- multi-label
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- classification
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pretty_name: WOS Hierarchical Multi-Label Text Classification
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size_categories:
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- 10K<n<100K
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---
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Introduced by du Toit and Dunaiski (2024) [Introducing Three New Benchmark Datasets for Hierarchical Text Classification](https://arxiv.org/abs/2411.19119).
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The WOS Hierarchical Text Classification are three dataset variants created from Web of Science (WOS) title and abstract data categorised into a hierarchical, multi-label class structure. The aim of the sampling and filtering methodology used was to create well-balanced class distributions (at chosen hierarchical levels). Furthermore, the WOS_JTF variant was also created with the aim to only contain publication data such that their class assignments results is classes instances that semantically more similar.
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The three dataset variants have the following properties:
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1. WOS_JT comprises 43,366 total samples (train=30356, dev=6505, test=6505) and only uses the journal-based classifications as labels.
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2. WOS_CT comprises 65,200 total samples (train=45640, dev=9780, test=9780) and only uses citation-based classifications as labels.
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3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification.
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