Evidence map›Paper›PMID 35463231›Full record

ArticleComputational intelligence and neuroscience2022

Intelligent Recognition Model of Business English Translation Based on Improved GLR Algorithm.

Laiying Deng, Xinde Hu, Fen Liu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 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 It has been retracted, and should not be counted.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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Laiying DengGuangzhou City Polytechnic, Guangzhou, Guangdong 510000, China.
Xinde HuBeijing Qingneng Internet Technology Co., Ltd, Guangzhou Branch, Guangzhou, Guangdong 510000, China.
Fen LiuGuangzhou City Polytechnic, Guangzhou, Guangdong 510000, China.ORCID https://orcid.org/0000-0002-7473-2277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming at the problem of low accuracy of traditional algorithm model, an intelligent recognition model of business English translation based on an improved GLR algorithm is proposed. Through this algorithm, the automatic sentence recognition technology is established, and according to the characteristics of business English, the improved GLR algorithm is used for collection, sorting, and analysis, so as to realize the intelligent recognition of business English. The results show that based on the improved GLR algorithm, the recognition accuracy is high, and the comprehensive score is 92.5 points, which overcomes the disadvantages of the GLR algorithm, and the operation speed and processing are improved. Based on the improved GLR algorithm, the intelligent translation of business English is realized, which is accurate and fast, and greatly promotes the learning and development of business English.

Indexed as

AlgorithmsNeural Networks, ComputerIntelligenceLanguageTranslations

Identifiers

PMID35463231
PMCPMC9033330

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.