Application of Adaptive Neural Network Algorithm Model in English Text Analysis.

Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model's ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers' composition correction.

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

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.

Association of Aurora-A (STK15) kinase polymorphisms with clinical outcome of esophageal cancer treated with preoperative chemoradiation.

BACKGROUND: Aurora-A/STK15 is a serine/threonine kinase critical for regulated chromosome segregation and cytokinesis. We investigated the association between 2 nonsynonymous single nucleotide polymorphisms in the coding region of STK15, T91A (Phe31Ile) and G169A (Val57Ile), and clinical outcome of esophageal cancer treated with preoperative chemoradiation. METHODS: Genotypes at Phe31Ile and Val57Ile were assessed from peripheral blood lymphocytes of 190 esophageal cancer patients and were correlated to response to treatment, recurrence rate, risk of death, disease-free survival (DFS) and median survival time (MTS). RESULTS: All patients had resectable esophageal or gastroesophageal junction cancer and received preoperative chemoradiation followed by esophagectomy. The heterozygous variant Phe31/Ile variant was significantly associated with tumor recurrence (odds ratio [OR] = 4.39; 95% confidence interval [CI], 2.12-8.94; P < .001), shorter DFS (P = .0001), and shorter MTS (P = .012). For patients receiving cisplatin-based therapy, only the variant Phe31/Ile had an adverse effect on response (OR = 2.8; 95% CI, 1.01-5.17; P = .048) and MTS (P = .026). The variant 91A-169G haplotype carried a significant risk for lack of complete response (OR = 2.54; 95% CI, 1.15-5.54) and higher rate of recurrence (OR = 2.73; 95%CI, 1.00-7.29). The presence of at least 1 variant allele at each locus further increased the risk of recurrence (adjusted OR = 6.21; 95% CI, 2.28-17.11; P = <.001), and was associated significantly shorter DFS (P = .003). CONCLUSIONS: Our study shows that functional SNPs in the STK15 gene are associated with higher rate of recurrence, higher likelihood of chemoratiotherapy-resistance, shorter DFS, and shorter MTS. Confirmation of our data and understanding the mechanisms through which STK15 functional SNPs mediate resistance to chemoradiotherapy are warranted.