Search Results for author: Petr Knoth

Found 17 papers, 6 papers with code

ACT2: A multi-disciplinary semi-structured dataset for importance and purpose classification of citations

1 code implementation LREC 2022 Suchetha Nambanoor Kunnath, Valentin Stauber, Ronin Wu, David Pride, Viktor Botev, Petr Knoth

This modified corpus has annotations for both citation function and importance classes newly enriched with supplementary contextual and non-contextual feature sets the selection of which follows from the lists of features used by the more successful teams in these shared tasks.

Overview of the 2021 SDP 3C Citation Context Classification Shared Task

no code implementations NAACL (sdp) 2021 Suchetha N. Kunnath, David Pride, Drahomira Herrmannova, Petr Knoth

The task is composed of two subtasks: classifying citations based on their (Subtask A) purpose and (Subtask B) influence.

Benchmark for Research Theme Classification of Scholarly Documents

1 code implementation sdp (COLING) 2022 Óscar E. Mendoza, Wojciech Kusa, Alaa El-Ebshihy, Ronin Wu, David Pride, Petr Knoth, Drahomira Herrmannova, Florina Piroi, Gabriella Pasi, Allan Hanbury

We present a new gold-standard dataset and a benchmark for the Research Theme Identification task, a sub-task of the Scholarly Knowledge Graph Generation shared task, at the 3rd Workshop on Scholarly Document Processing.

Classification Graph Generation

CRUISE-Screening: Living Literature Reviews Toolbox

1 code implementation4 Sep 2023 Wojciech Kusa, Petr Knoth, Allan Hanbury

To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews - a type of literature review that is continuously updated to reflect the latest research in a particular field.

Question Answering text-classification +1

CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering

1 code implementation6 Jul 2023 David Pride, Matteo Cancellieri, Petr Knoth

CORE-GPT's performance was evaluated on a dataset of 100 questions covering the top 20 scientific domains in CORE, resulting in 100 answers and links to 500 relevant articles.

Question Answering

Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking

no code implementations1 Jul 2023 Wojciech Kusa, Óscar E. Mendoza, Petr Knoth, Gabriella Pasi, Allan Hanbury

Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both queries and documents during the first retrieval stage, and (ii) a novel re-ranking schema that uses a Transformer network in a setup adapted to this task by leveraging the structure of the CT documents.

Descriptive named-entity-recognition +5

Outcome-based Evaluation of Systematic Review Automation

no code implementations30 Jun 2023 Wojciech Kusa, Guido Zuccon, Petr Knoth, Allan Hanbury

We find that accounting for the difference in review outcomes leads to a different assessment of the quality of a system than if traditional evaluation measures were used.

TAR

Predicting article quality scores with machine learning: The UK Research Excellence Framework

no code implementations11 Dec 2022 Mike Thelwall, Kayvan Kousha, Mahshid Abdoli, Emma Stuart, Meiko Makita, Paul Wilson, Jonathan Levitt, Petr Knoth, Matteo Cancellieri

National research evaluation initiatives and incentive schemes have previously chosen between simplistic quantitative indicators and time-consuming peer review, sometimes supported by bibliometrics.

Active Learning

Confidence estimation of classification based on the distribution of the neural network output layer

no code implementations14 Oct 2022 Abdel Aziz Taha, Leonhard Hennig, Petr Knoth

In this paper, we propose novel methods that, given a neural network classification model, estimate uncertainty of particular predictions generated by this model.

Image Classification named-entity-recognition +3

Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study

1 code implementation19 Jan 2022 Wojciech Kusa, Allan Hanbury, Petr Knoth

In this work, we conduct a replicability study of the first two deep learning papers for citation screening and evaluate their performance on 23 publicly available datasets.

Document Classification Word Embeddings

Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations

no code implementations19 Jul 2018 Joeran Beel, Andrew Collins, Oliver Kopp, Linus W. Dietz, Petr Knoth

We present the architecture of Mr. DLib's living lab as well as usage statistics on the first sixteen months of operating it.

Management Recommendation Systems

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