11,935 research outputs found
Automatically detecting open academic review praise and criticism
This is an accepted manuscript of an article published by Emerald in Online Information Review on 15 June 2020.
The accepted version of the publication may differ from the final published version, accessible at https://doi.org/10.1108/OIR-11-2019-0347.Purpose: Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research’s open peer review publishing platform.
Design/methodology/approach: PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.
Findings: PeerJudge can predict F1000Research judgements from negative evaluations in reviewers’ comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.
Originality/value: PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments
The Closer the Better: Similarity of Publication Pairs at Different Co-Citation Levels
We investigate the similarities of pairs of articles which are co-cited at
the different co-citation levels of the journal, article, section, paragraph,
sentence and bracket. Our results indicate that textual similarity,
intellectual overlap (shared references), author overlap (shared authors),
proximity in publication time all rise monotonically as the co-citation level
gets lower (from journal to bracket). While the main gain in similarity happens
when moving from journal to article co-citation, all level changes entail an
increase in similarity, especially section to paragraph and paragraph to
sentence/bracket levels. We compare results from four journals over the years
2010-2015: Cell, the European Journal of Operational Research, Physics Letters
B and Research Policy, with consistent general outcomes and some interesting
differences. Our findings motivate the use of granular co-citation information
as defined by meaningful units of text, with implications for, among others,
the elaboration of maps of science and the retrieval of scholarly literature
PubMed and Beyond: Recent Advances and Best Practices in Biomedical Literature Search
Biomedical research yields a wealth of information, much of which is only
accessible through the literature. Consequently, literature search is an
essential tool for building on prior knowledge in clinical and biomedical
research. Although recent improvements in artificial intelligence have expanded
functionality beyond keyword-based search, these advances may be unfamiliar to
clinicians and researchers. In response, we present a survey of literature
search tools tailored to both general and specific information needs in
biomedicine, with the objective of helping readers efficiently fulfill their
information needs. We first examine the widely used PubMed search engine,
discussing recent improvements and continued challenges. We then describe
literature search tools catering to five specific information needs: 1.
Identifying high-quality clinical research for evidence-based medicine. 2.
Retrieving gene-related information for precision medicine and genomics. 3.
Searching by meaning, including natural language questions. 4. Locating related
articles with literature recommendation. 5. Mining literature to discover
associations between concepts such as diseases and genetic variants.
Additionally, we cover practical considerations and best practices for choosing
and using these tools. Finally, we provide a perspective on the future of
literature search engines, considering recent breakthroughs in large language
models such as ChatGPT. In summary, our survey provides a comprehensive view of
biomedical literature search functionalities with 36 publicly available tools.Comment: 27 pages, 6 figures, 36 tool
A Literature Review on Intelligent Services Applied to Distance Learning
Distance learning has assumed a relevant role in the educational scenario. The use of
Virtual Learning Environments contributes to obtaining a substantial amount of educational data.
In this sense, the analyzed data generate knowledge used by institutions to assist managers and
professors in strategic planning and teaching. The discovery of students’ behaviors enables a wide
variety of intelligent services for assisting in the learning process. This article presents a literature
review in order to identify the intelligent services applied in distance learning. The research covers
the period from January 2010 to May 2021. The initial search found 1316 articles, among which
51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning
systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems
or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the
principal services offered are recommendation systems and learning systems. In these services, the
analysis of student profiles stands out to identify patterns of behavior, detect low performance, and
identify probabilities of dropouts from courses.info:eu-repo/semantics/publishedVersio
- …