ArticleBMC bioinformatics2010
The structural and content aspects of abstracts versus bodies of full text journal articles are different.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using text mining for study identification in systematic reviews: a systematic review of current approaches.Systematic reviews · 2015Pooled it
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- Supporting the working life exposome: Annotating occupational exposure for enhanced literature search.PloS one · 2024Article
- Unsupervised learning and natural language processing highlight research trends in a superbug.Frontiers in artificial intelligence · 2024Article
- Towards a unified search: Improving PubMed retrieval with full text.Journal of biomedical informatics · 2022Article
- Examining linguistic shifts between preprints and publications.PLoS biology · 2022Article
- COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19.Journal of chemical information and modeling · 2021Article
- ResidueFinder: extracting individual residue mentions from protein literature.Journal of biomedical semantics · 2021Article
- Text mining for modeling of protein complexes enhanced by machine learning.Bioinformatics (Oxford, England) · 2021Article
- COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19.ChemRxiv : the preprint server for chemistry · 2020Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- LitSense: making sense of biomedical literature at sentence level.Nucleic acids research · 2019Article
- PubTator central: automated concept annotation for biomedical full text articles.Nucleic acids research · 2019Article
- From POS tagging to dependency parsing for biomedical event extraction.BMC bioinformatics · 2019Article
- Automated, Efficient, and Accelerated Knowledge Modeling of the Cognitive Neuroimaging Literature Using the ATHENA Toolkit.Frontiers in neuroscience · 2019Article
- Biomedical text mining for research rigor and integrity: tasks, challenges, directions.Briefings in bioinformatics · 2018Article
- Three Dimensions of Reproducibility in Natural Language Processing.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2018Article
- Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature.BMC bioinformatics · 2018Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundAn increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research.
resultsWe examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies.
conclusionsAspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.