Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning

• URL: http://arxiv.org/abs/2302.00390v3
• Date: Thu, 25 Jul 2024 02:02:58 GMT
• Title: Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning
• Authors: Susie Xi Rao, Peter H. Egger, Ce Zhang,
• Abstract summary: This paper presents a hierarchical classification system that automatically categorizes a scholarly publication using its abstract. It distinguishes 44 disciplines, 718 fields and 1,485 subfields among 160 million abstract snippets in Microsoft Academic Graph. The classification accuracy is > 90% in 77.13% and 78.19% of the single-label and multi-label classifications, respectively.
• Score: 15.915719490494876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a hierarchical classification system that automatically categorizes a scholarly publication using its abstract into a three-tier hierarchical label set (discipline, field, subfield) in a multi-class setting. This system enables a holistic categorization of research activities in the mentioned hierarchy in terms of knowledge production through articles and impact through citations, permitting those activities to fall into multiple categories. The classification system distinguishes 44 disciplines, 718 fields and 1,485 subfields among 160 million abstract snippets in Microsoft Academic Graph (version 2018-05-17). We used batch training in a modularized and distributed fashion to address and allow for interdisciplinary and interfield classifications in single-label and multi-label settings. In total, we have conducted 3,140 experiments in all considered models (Convolutional Neural Networks, Recurrent Neural Networks, Transformers). The classification accuracy is > 90% in 77.13% and 78.19% of the single-label and multi-label classifications, respectively. We examine the advantages of our classification by its ability to better align research texts and output with disciplines, to adequately classify them in an automated way, and to capture the degree of interdisciplinarity. The proposed system (a set of pre-trained models) can serve as a backbone to an interactive system for indexing scientific publications in the future.

Related papers

• Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions [62.12545440385489]
  Large language models (LLMs) have brought substantial advancements in text generation, but their potential for enhancing classification tasks remains underexplored. We propose a framework for thoroughly investigating fine-tuning LLMs for classification, including both generation- and encoding-based approaches. We instantiate this framework in edit intent classification (EIC), a challenging and underexplored classification task.
  arXiv  Detail & Related papers  (2024-10-02T20:48:28Z)
• Multi-Label Requirements Classification with Large Taxonomies [40.588683959176116]
  Multi-label requirements classification with large labels could aid requirements traceability but is prohibitively costly with supervised training. We associated 129 requirements with 769 labels from ranging between 250 and 1183 classes. The sentence-based classification had a significantly higher recall compared to the word-based classification. The hierarchical classification strategy did not always improve the performance of requirements classification.
  arXiv  Detail & Related papers  (2024-06-07T09:53:55Z)
• Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling [0.0]
  This paper introduces a novel approach using the SciBERT model and CNNs to systematically categorize academic abstracts. The CNN uses convolution and pooling to enhance feature extraction and reduce dimensionality.
  arXiv  Detail & Related papers  (2024-04-16T05:21:47Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Hierarchical Multi-Label Classification of Scientific Documents [47.293189105900524]
  We introduce a new dataset for hierarchical multi-label text classification of scientific papers called SciHTC. This dataset contains 186,160 papers and 1,233 categories from the ACM CCS tree. Our best model achieves a Macro-F1 score of 34.57% which shows that this dataset provides significant research opportunities.
  arXiv  Detail & Related papers  (2022-11-05T04:12:57Z)
• Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework [75.79736930414715]
  We present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint.
  arXiv  Detail & Related papers  (2022-04-27T21:41:44Z)
• SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications [0.7305019142196583]
  Classifying scientific publications according to Field-of-Science (FoS) is of crucial importance. We present SciNoBo, a novel classification system of publications to predefined FoS. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multi-arity potential.
  arXiv  Detail & Related papers  (2022-04-02T15:09:33Z)
• TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters [57.59286394188025]
  We propose a novel framework for topic taxonomy completion, named TaxoCom. TaxoCom discovers novel sub-topic clusters of terms and documents. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage.
  arXiv  Detail & Related papers  (2022-01-18T07:07:38Z)
• Inducing a hierarchy for multi-class classification problems [11.58041597483471]
  In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not. In this paper, we investigate a class of methods that induce a hierarchy that can similarly improve classification performance over flat classifiers. We demonstrate the effectiveness of the class of methods both for discovering a latent hierarchy and for improving accuracy in principled simulation settings and three real data applications.
  arXiv  Detail & Related papers  (2021-02-20T05:40:42Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.