ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
A literature-based assessment of concept pairs as a measure of semantic relatedness.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.Journal of biomedical informatics · 2019Article
- Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation.Journal of biomedical discovery and collaboration · 2016Article
- U-path: An undirected path-based measure of semantic similarity.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The semantic relatedness between two concepts, according to human perception, is domain-rooted and reflects prior knowledge. We developed a new method for semantic relatedness assessment that reflects human judgment, utilizing semantic predications extracted from PubMed citations by SemRep. We compared the new method to other approaches utilizing path-based, statistical, and context vector methods, using a gold standard for evaluation. The new method outperformed all others, except one variation of the context vector technique. These findings have implications in several natural language processing applications, such as serendipitous knowledge discovery.
Indexed as
Identifiers
24551423PMC3900161What 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.