Hierarchical Multi-Label Classification of Scientific Documents

Sadat, Mobashir; Caragea, Cornelia

doi:10.18653/v1/2022.emnlp-main.610

Cited by 3 publications

(1 citation statement)

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“…Scholarly keyphrase boundary classification is the task of identifying highly summative phrases from scientific papers and classifying them into a set of pre-defined classes Augenstein and Søgaard, 2017). In a scientific domain, keyphrases and their classes, e.g., task, process, or material, are critical for many downstream applications including effectively mining, searching, and analyzing the scientific literature ; promoting an efficient understanding of what methods, processes, tasks, or resources are being used or proposed in a given paper and how to track them over time (Uban et al, 2021); scientific paper summarization (Abu-Jbara and Radev, 2011;Qazvinian et al, 2010); keyphrase-based question answering (Quarteroni and Manandhar, 2006); machine comprehension (Subramanian et al, 2018); scientific paper rec-ommendation (Chen et al, 2015); topic classification (Sadat and Caragea, 2022;Onan et al, 2016;Caragea et al, 2015); and, more broadly, data augmentation for NLP tasks (Li et al, 2023a,b).…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification

Park,

Caragea

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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The task of scholarly keyphrase boundary classification aims at identifying keyphrases from scientific papers and classifying them with their types from a set of predefined classes (e.g., task, process, or material). Despite the importance of keyphrases and their types in many downstream applications including indexing, searching, and question answering over scientific documents, scholarly keyphrase boundary classification is still an under-explored task. In this work, we propose a novel embedding constraint on multitask knowledge distillation which enforces the teachers (single-task models) and the student (multi-task model) similarity in the embedding space. Specifically, we enforce that the student model is trained not only to imitate the teachers' output distribution over classes, but also to produce language representations that are similar to those produced by the teachers. Our results show that the proposed approach outperforms previous works and strong baselines on three datasets of scientific documents.

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Section: Introductionmentioning

confidence: 99%

Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification

Park,

Caragea

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Hierarchical Multi-label Classification of Online Vaccine Concerns

Qinyu Zhu,

Stureborg,

Dhingra

2024

Studies in Computational Intelligence

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Run-Time Prevention of Software Integration Failures of Machine Learning APIs

Wan,

Liu,

et al. 2023

Proc. ACM Program. Lang.

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Due to the under-specified interfaces, developers face challenges in correctly integrating machine learning (ML) APIs in software. Even when the ML API and the software are well designed on their own, the resulting application misbehaves when the API output is incompatible with the software. It is desirable to have an adapter that converts ML API output at runtime to better fit the software need and prevent integration failures. In this paper, we conduct an empirical study to understand ML API integration problems in real-world applications. Guided by this study, we present SmartGear, a tool that automatically detects and converts mismatching or incorrect ML API output at run time, serving as a middle layer between ML API and software. Our evaluation on a variety of open-source applications shows that SmartGear detects 70% incompatible API outputs and prevents 67% potential integration failures, outperforming alternative solutions.

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Hierarchical Multi-Label Classification of Scientific Documents

Cited by 3 publications

References 0 publications

Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification

Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification

Hierarchical Multi-label Classification of Online Vaccine Concerns

Run-Time Prevention of Software Integration Failures of Machine Learning APIs

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