Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Abstract

This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.

The impact of internal-generated contextual clues on EFL vocabulary learning: insights from EEG

“…Given that understanding provided contexts relies on learners' prior language proficiency, example sentences may hinder semantic processing and contextual integration due to poor linguistic comprehensibility caused by the presence of unfamiliar words in the context (Bernardo and Harris, 2017;Elgort et al, 2018b). It further impedes learners' vocabulary acquisition if example sentences are created by automatic machine translation that lacks the richness of expression (Hsiao and Hung, 2022). Learners might, therefore, achieve better performance by generating their contextual clues and linking words to their existing semantic networks (Ding et al, 2017).…”

Semantic Coherence Analysis of English Texts Based on Sentence Semantic Graphs

“…As a result, researchers have begun to investigate and quantify the quality of text coherence with a view to its practical application, and this has led to the study of text coherence. Generally speaking, when scoring English language learners' essays, the scoring criteria should cover four aspects: lexical complexity, grammatical accuracy, syntactic complexity and discourse coherence [4], which are the prerequisites for accurate and reliable scoring results. However, existing automatic English essay scoring systems rarely address the indicator of coherence, which results in an unreasonable final score for English essays.…”

Retracted: Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”