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Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews.
Frontiers in Public Health, 2022
Background: The COVID-pandemic prompted the scientific community to share timely evidence, also in the form of pre-printed papers, not peer reviewed yet. Purpose: To develop an artificial intelligence system for the analysis of the scientific literature by leveraging on recent developments in the field of Argument Mining. Methodology: Scientific quality criteria were borrowed from two selected Cochrane systematic reviews. Four independent reviewers gave a blind evaluation on a-scale to papers for each review. These scores were matched with the automatic analysis performed by an AM system named MARGOT, which detected claims and supporting evidence for the cited papers. Outcomes were evaluated with inter-rater indices (Cohen's Kappa, Krippendor 's Alpha, s* statistics). Results: MARGOT performs di erently on the two selected Cochrane reviews: the inter-rater indices show a fair-to-moderate agreement of the most relevant MARGOT metrics both with Cochrane and the skilled interval scores, with larger values for one of the two reviews. Discussion and conclusions: The noted discrepancy could rely on a limitation of the MARGOT system that can be improved; yet, the level of agreement between human reviewers also suggests a di erent complexity between the two reviews in debating controversial arguments. These preliminary results encourage to expand and deepen the investigation to other topics and a larger number of highly specialized reviewers, to reduce uncertainty in the evaluation process, thus supporting the retraining of AM systems.
Quantitative Science Studies, 2021
The unprecedented mobilization of scientists, consequent of the COVID-19 pandemics, has generated an enormous number of scholarly articles that is impossible for a human being to keep track and explore without appropriate tool support. In this context, we created the Covid-on-the-Web project, which aims to assist the access, querying, and sense making of COVID-19 related literature by combining efforts from semantic web, natural language processing, and visualization fields. Particularly, in this paper, we present (i) an RDF dataset, a linked version of the “COVID-19 Open Research Dataset” (CORD-19), enriched via entity linking and argument mining, and (ii) the “Linked Data Visualizer” (LDViz), 28 which assists the querying and visual exploration of the referred dataset. The LDViz tool assists the exploration of different views of the data by combining a querying management interface, which enables the definition of meaningful subsets of data through SPARQL queries, and a visualizat...
2021
In this work we propose to tackle the limitations posed by the lack of annotated data for argument mining in scientific texts by annotating argumentative units and relations in research abstracts in two scientific domains. We evaluate our annotations by computing inter-annotator agreements, which range from moderate to substantial according to the difficulty level of the tasks and domains. We use our newly annotated corpus to fine-tune BERT-based models for argument mining in single and multi-task settings, finally exploring the adaptation of models trained in one scientific discipline (computational linguistics) to predict the argumentative structure of abstracts in a different one (biomedicine).
2021
Argument mining consists in the automatic identification of argumentative structures in natural language, a task that has been recognized as particularly challenging in the scientific domain. In this work we propose SciARG, a new annotation scheme, and apply it to the identification of argumentative units and relations in abstracts in two scientific disciplines: computational linguistics and biomedicine, which allows us to assess the applicability of our scheme to different knowledge fields. We use our annotated corpus to train and evaluate argument mining models in various experimental settings, including single and multi-task learning. We investigate the possibility of leveraging existing annotations, including discourse relations and rhetorical roles of sentences, to improve the performance of argument mining models. In particular, we explore the potential offered by a sequential transfer- learning approach in which supplementary training tasks are used to fine-tune pre-trained p...
Procedia Computer Science, 2020
COVID-19 is one of the most important topic these days, specifically on search engines and news. While fake news are easily shared, scientific papers are reliable sources where information can be extracted. With about 24,000 scientific publications on COVID-19 and related research on PUBMED, automatic computer-assisted analysis is required. In this paper, we develop two methodologies to get insights on specific sub-topics of interest and latest research sub-topics. They rely on natural language processing and graph-based visualizations. We run these methodologies on two cases: the virus origin and the uses of existing drugs.
2021
Background: Highly cited papers are considered publications with a great impact on a scientific community and have been deeply investigated in different fields. Aim: This study aimed at analyzing and visualizing the top 1000 highly cited papers on COVID-19. Methods: As a bibliometric study, this study was conducted by retrieving 1000 highly-cited papers on COVID-19 published during 2019-2021 from Scopus. The search strategy was to obtain 35 related keywords/terms on the COVID-19 as the main term from MeSH and searching them in the fields of paper titles, abstracts, and keywords. Bibliometric techniques such as co-citation analysis, co-authorship analysis and word co-occurrence analysis were used for the study. Data visualization was done by applying the VOSviewer software package and GunnMap. Results: China, the United States of America (USA), and the United Kingdom (UK) with publishing 418, 353, and 149, mostly cited papers were ranked first-to-third, respectively. The top contribu...
International Journal of Evaluation and Research in Education (IJERE) , 2024
The topic of scientific arguments is crucial to discuss because it is one of the basic sciences that has close links with other topics. Moreover, this science has a close relationship with the skills of the 21st century today. This study aims to reveal the current trends in the scientific argumentation field for the last 10 years based on the Scopus database. This study uses quantitative research through bibliometric studies with the keyword scientific argumentation year. The results of this study indicate that the number of article publications during the years 2012 to 2021 on the topic of scientific argumentation has increased on average. However, the most visible increase was in 2018-2021. The United States, Indonesia, and Germany dominated the publication of scientific argumentation topics from 2012 to 2021. As for the top authors were from the United States and Germany. Furthermore, in scientific argumentation, dominated by subject areas based on science, keywords used are argumentation, students, and scientific argumentation. There were several suggestions for the research, namely: i) the need for further research, especially on the differences and similarities of each argumentation; ii) the need to discuss the appropriate scope of argumentation at the appropriate learning level; iii) collaboration between universities that have a focus on this argumentation field. It is highly recommended that the research be more robust; and iv) future research must adapt to the current development of arguments so that the topic does not decline but continues to become the basis for studying other sciences.
2020
Coronavirus disease (COVID-19) has been declared as a pandemic by WHO with thousands of cases being reported each day. Thousands of scientific articles are being published on the disease raising the need for a service which can organize, and query them in a reliable fashion. To support this cause, we present AWS CORD-19 Search (ACS), a public COVID-19 specific search engine that is powered by machine learning. ACS with its capabilities such as topic based, natural language search queries, and reading comprehension and FAQ matching provides a scalable solution to COVID-19 researchers and policy makers in their search and discovery for answers to high priority scientific questions. We present evaluation and qualitative analysis of the system with specific examples to illustrate the capabilities of the system.
Quantitative Science Studies
Since the beginning of the 2019–20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies, or even government actions to limit the spread of the disease and directing treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge challenging. In this work, we describe BIP4COVID19, an open dataset that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search...
Proceedings of the 8th Workshop on Argument Mining, 2021
Science, technology and innovation (STI) policies have evolved in the past decade. We are now progressing towards policies that are more aligned with sustainable development through integrating social, economic and environmental dimensions. In this new policy environment, the need to keep track of innovation from its conception in Science and Research has emerged. Argumentation mining, an interdisciplinary NLP field, gives rise to the required technologies. In this study, we present the first STI-driven multidisciplinary corpus of scientific abstracts annotated for argumentative units (AUs) on the sustainable development goals (SDGs) set by the United Nations (UN). AUs are the sentences conveying the Claim(s) reported in the author's original research and the Evidence provided for support. We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70.0 for Claim and 62.4 for Evidence identification evaluated with 10-fold cross-validation. To demonstrate the effectiveness of our models, we experiment with different test sets showing comparable performance across various SDG policy domains. Our dataset and models are publicly available for research purposes 1 .
International Journal of Medical Informatics, 2006
The aim of this study is to investigate the relationships between citations and the scientific argumentation found in the abstract. We extracted citation lists from a set of 3200 full-text papers originating from a narrow domain.
Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications - JNLPBA '04, 2004
The aim of this study is to investigate the relationships between citations and the scientific argumentation found in the abstract. We extracted citation lists from a set of 3200 full-text papers originating from a narrow domain.
2020
Scientists are harnessing their multidisciplinary expertise and resources to fight the COVID-19 pandemic. Aligned with this mind-set, the Covid-on-the-Web project aims to allow biomedical researchers to access, query and make sense of COVID-19 related literature. To do so, it adapts, combines and extends tools to process, analyze and enrich the "COVID-19 Open Research Dataset" (CORD-19) that gathers 50,000+ full-text scientific articles related to the coronaviruses. We report on the RDF dataset and software resources produced in this project by leveraging skills in knowledge representation, text, data and argument mining, as well as data visualization and exploration. The dataset comprises two main knowledge graphs describing (1) named entities mentioned in the CORD-19 corpus and linked to DBpedia, Wikidata and other BioPortal vocabularies, and (2) arguments extracted using ACTA, a tool automating the extraction and visualization of argumentative graphs, meant to help clin...
2020
BackgroundIn the attempt to face the COVID-19 pandemic, the global scientific community has been expending great efforts to produce useful and reliable data aiming to help patients, physicians and guiding public health policies. A huge amount of information is being released every week, making impossible for a single person (or even for a research group) to read everything and get constantly updated on the scientific literature concerning COVID-19 and its etiological agent, SARS-CoV-2. Therefore, we developed PlatCOVID (www.platcovid.com), a Web platform designed to analyze, cluster, classify and discuss COVID-19 literature available on LitCovid (NCBI). ResultsPlatCOVID has been created as a novel COVID-19 hub able to add features of text mining and syntax analyses methods, such as word and sentence atomization and tokenization, clusterization and classification. The main division of the literature comprehends five categories: 1) Diagnosis; 2) Epidemiology; 3) Clinical, Signs & Symp...
Lecture Notes in Computer Science, 2019
Argument mining consists in the automatic identification of argumentative structures in texts. In this work we address the open question of whether discourse-level annotations can contribute to facilitate the identification of argumentative components and relations in scientific literature. We conduct a pilot study by enriching a corpus of computational linguistics abstracts that contains discourse annotations with a new argumentative annotation level. The results obtained from preliminary experiments confirm the potential value of the proposed approach.
Proceedings of the Second Workshop on Scholarly Document Processing
Argument mining targets structures in natural language related to interpretation and persuasion. Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions, which could benefit from argument mining techniques. However, While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce. This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications. We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.
Journal of Information Science, 2021
The purpose of this study is to develop a text clustering–based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation–maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest.
ArXiv, 2020
A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text mining in order to find the most interestingness words, on the one hand, and relationships between them on the other. Then, a method, called information cartography, was applied for extracting structured knowledge from a huge amount of association rules. On the basis of these methods, the purpose of our study was to show how researchers have responded in similar epidemic/pandemic situations throughout history.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2007
PROBLEM: Key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles and to help retrieving biomedical articles. Abstracts are also used for this purpose. However with usually more than 300 words, MEDLINE abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into 4 argumentative moves: PURPOSE, METHODS, RESULTS and CONCLUSION. METHODS: We rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification using Bayesian learners is feasible on MEDLINE abstracts and should help user navigation in such repositories.
International Journal of Environmental Research and Public Health, 2022
The COVID-19 pandemic continues to cause a collapse in the health systems and econo-mies of many countries around the world, after 2 years of struggle and with the number of cases still growing exponentially. Health communication has become as essential and necessary for control of the pandemic as epidemiology. This bibliometric analysis identifies existing contributions, jointly studying health communication and the pandemic in scientific journals indexed. A systematic search of the Web of Science was performed, using keywords related to COVID-19 and health communication. Data extracted included the type of study, journal, number of citations, number of authors, country of publication, and study content. As the number of scientific investigations has grown, it is necessary to delve into the areas in which the most impactful publications have been generated. The results show that the scientific community has been quick to react by generating an extraordinary volume of publications. ...
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