Mining arguments in scientific abstracts with discourse-level embeddings

Accuosto, Pablo; Saggion, Horacio

doi:10.1016/j.datak.2020.101840

Cited by 8 publications

(6 citation statements)

References 47 publications

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“…The results show improvements in the rhetorical tasks, but not in AM. Accuosto and Saggion [22] experiment with MTL and sequential transfer learning, improving performance on AM through discourse parsing tasks.…”

Section: A Multi-task Learning and Joint Learning For Ammentioning

confidence: 99%

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Multi-Task Attentive Residual Networks for Argument Mining

Galassi

Lippi

Torroni

2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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We explore the use of residual networks and neural attention for multiple argument mining tasks. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble, without any assumption on document or argument structure. We present an extensive experimental evaluation on five different corpora of user-generated comments, scientific publications, and persuasive essays. Our results show that our approach is a strong competitor against state-of-the-art architectures with a higher computational footprint or corpus-specific design, representing an interesting compromise between generality, performance accuracy and reduced model size.

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Section: A Multi-task Learning and Joint Learning For Ammentioning

confidence: 99%

“…The SciDTB Argumentative Corpus [22] consists of 60 scientific abstracts from the ACL anthology, for a total of 353 argumentative components. Components can span across multiple sentences and can belong to six classes: PROPOSAL (110), AS-SERTION (88), RESULT (64), OBSERVATION (11), MEANS (63), DESCRIPTION (7).…”

Section: Scidtbmentioning

confidence: 99%

Multi-Task Attentive Residual Networks for Argument Mining

Galassi

Lippi

Torroni

2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…We focus on five differently annotated corpora: AAEC, a medical abstract corpus named Abs-tRCT (Mayer et al, 2020), 1 CDCP, MTC, and the argument-annotated SciDTB (AASD; Accuosto and Saggion, 2020). These corpora are useful for discussing differences and similarities in argument structure.…”

Section: Overview Of Am Corporamentioning

confidence: 99%

“…AASD (Accuosto and Saggion, 2020) was created to address the lack of a scientific AM corpus. The authors enriched annotations for a subset of SciDTB (Yang and Li, 2018) by providing component types for Proposal, Assertion, Result, Observation, Means, and Description.…”

Section: Overview Of Am Corporamentioning

confidence: 99%

End-to-end Argument Mining with Cross-corpora Multi-task Learning

Morio

Ozaki

Morishita

et al. 2022

Transactions of the Association for Computational Linguistics

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Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.

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“…Moreover, the data elements used by existing scientific literature classification systems are primarily derived from explicit scientometric information such as titles, abstracts, and keywords. Nevertheless, data elements may also have implicit relationships such as journal names, authors, research institution names, and research content and method [6,10,18,19]. The same journal, author, or research institution are more likely to focus on certain research content and methods, even if there is no direct relationship between them.…”

Section: Introductionmentioning

confidence: 99%

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

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With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.

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Mining arguments in scientific abstracts with discourse-level embeddings

Cited by 8 publications

References 47 publications

Multi-Task Attentive Residual Networks for Argument Mining

Multi-Task Attentive Residual Networks for Argument Mining

End-to-end Argument Mining with Cross-corpora Multi-task Learning

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

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