ArticleJournal of environmental and public health2022
Text Score Analysis under the IPE Environment Based on Improved Transformer.
Article in Journal of environmental and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Unmasking the challenges in ideological and political education in China: A thematic review.Heliyon · 2024Review
- Retracted: Text Score Analysis under the IPE Environment Based on Improved Transformer.Journal of environmental and public health · 2023Article
Corrections and comments
- Retraction · 2023-06-28Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to improve the accuracy of ideological and political education (IPE) text scoring, an improved short-text similarity calculation model based on transformer is proposed. This model takes the DSSM model as the basic framework and uses the Bert model to realize text representation and solve polysemy problem. The transformer encoding component is used to extract the characteristics of the text and obtain the internal information of the text. With the help of the encoding component, the two texts can interact with information on multiple levels. Finally, the semantic similarity between two texts is calculated by concatenation vector inference.
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.