scholarly journals Research on Intelligent English Translation Method Based on the Improved Attention Mechanism Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Rong Wang
Keyword(s):  
Neural Network ◽  
English Translation ◽  
Attention Mechanism ◽  
Machine Learning Algorithms ◽  
Translation System ◽  
Test Results ◽  
Long Distance ◽  
Distance Information ◽  
Mechanism Model ◽  
Translation Accuracy

The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.

scholarly journals Efficient English Translation Method and Analysis Based on the Hybrid Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chuncheng Wang
Keyword(s):  
Neural Network ◽  
English Translation ◽  
Short Term Memory ◽  
Chinese Translation ◽  
Long Distance ◽  
Hybrid Neural Network ◽  
Distance Information ◽  
Neural Machine Translation ◽  
Machine Learning Model ◽  
Translation Accuracy

Neural machine translation has been widely concerned in recent years. The traditional sequential neural network framework of English translation has obvious disadvantages because of its poor ability to capture long-distance information, and the current improved framework, such as the recurrent neural network, still cannot solve this problem very well. In this paper, we propose a hybrid neural network that combines the convolutional neural network (CNN) and long short-term memory (LSTM) and introduce the attention mechanism based on the encoder-decoder structure to improve the translation accuracy, especially for long sentences. In the experiment, this model is implemented based on TensorFlow, and the results show that the BLEU value of the proposed method is obviously improved compared with the traditional machine learning model, which proves the effectiveness of our method in English-Chinese translation.

Answer Selection in Community Question Answering Using LSTM

2021 ◽  
pp. 153-165
Author(s):  
Saman Qureshi ◽  
Sri Khetwat Saritha ◽  
D. Kishan
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Attention Mechanism ◽  
Generalization Ability ◽  
Mechanism Model ◽  
Community Question Answering ◽  
Informative Part ◽  
External Sources

Websites like Quora, Yahoo! Answers, and Reddit are examples of community question answering (CQA) systems that enable users to ask questions as well as to answer questions. Answer selection is the most challenging task in CQA systems to get the good and relevant answer for the user questions. The shortcomings in the current approaches are lexical gap between text pairs, dependency on external sources, and manual features which lead to lack of generalization ability. These shortcomings are resolved by already proposed work, but they lack generalization, and their performance is not satisfying. Whereas to focus on rich quality answers, attention mechanism can be integrates with neural network. This chapter proposes two models BLSTM and BLSTM with attention mechanism. Attention mechanism aligns question to the answer with the answer's more informative part. So, when it is applied in the model, BLSTM with attention mechanism model surpasses the top approaches.

scholarly journals Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Guangjun Dong ◽  
Youchao Yang ◽  
Qiankun Zhang
Keyword(s):  
Feature Extraction ◽  
English Translation ◽  
Selection Process ◽  
Translation System ◽  
Chinese Translation ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Optimal Feature Selection ◽  
Translation Accuracy ◽  
Optimal Feature

In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

scholarly journals Open Relation Extraction in Patent Claims with a Hybrid Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Boting Geng
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Language Processing ◽  
Network Architecture ◽  
Complex Structure ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Hybrid Network ◽  
Long Distance ◽  
Hybrid Neural Network

Research on relation extraction from patent documents, a high-priority topic of natural language process in recent years, is of great significance to a series of patent downstream applications, such as patent content mining, patent retrieval, and patent knowledge base constructions. Due to lengthy sentences, crossdomain technical terms, and complex structure of patent claims, it is extremely difficult to extract open triples with traditional methods of Natural Language Processing (NLP) parsers. In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship triples from patent claims with a hybrid neural network architecture based on multihead attention mechanism. The hybrid neural network framework combined with Bi-LSTM and CNN is proposed to extract argument phrase features and relation phrase features simultaneously. The Bi-LSTM network gains long distance dependency features, and the CNN obtains local content feature; then, multihead attention mechanism is applied to get potential dependency relationship for time series of RNN model; the result of neural network proposed above applied to our constructed open patent relation dataset shows that our method outperforms both traditional classification algorithms of machine learning and the-state-of-art neural network classification models in the measures of Precision, Recall, and F1.

Gated Hierarchical LSTMs for Target-Based Sentiment Analysis

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1719-1737
Author(s):  
Hao Wang ◽  
Xiaofang Zhang ◽  
Bin Liang ◽  
Qian Zhou ◽  
Baowen Xu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Model ◽  
Attention Mechanism ◽  
Support Vector ◽  
Neural Network Models ◽  
Long Distance ◽  
Sentence Level

In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.

scholarly journals An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records

2019 ◽  
Vol 19 (S5) ◽  
Author(s):  
Luqi Li ◽  
Jie Zhao ◽  
Li Hou ◽  
Yunkai Zhai ◽  
Jinming Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Named Entity Recognition ◽  
Attention Mechanism ◽  
Entity Recognition ◽  
Long Distance ◽  
Named Entity ◽  
Text Features

Abstract Background Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus. Methods From the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model. Results Based on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall. Conclusions Our work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.

scholarly journals Track Prediction for HF Radar Vessels Submerged in Strong Clutter Based on MSCNN Fusion with GRU-AM and AR Model

2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Ling Zhang ◽  
Jingzhi Zhang ◽  
Jiong Niu ◽  
Q. M. Jonathan Wu ◽  
Gangsheng Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Hf Radar ◽  
Ar Model ◽  
Trajectory Prediction ◽  
Long Distance ◽  
Multi Scale ◽  
Wave Radar ◽  
Wide Range

High-frequency (HF) surface-wave radar has a wide range of applications in marine monitoring due to its long-distance, wide-area, and all-weather detection ability. However, the accurate detection of HF radar vessels is severely restricted by strong clutter and interference, causing the echo of vessels completely submerged by clutter. As a result, the target cannot be detected and tracked for a period of time under the influence of strong clutter, which causes broken trajectories. To solve this problem, we propose an HF radar-vessel trajectory-prediction method based on a multi-scale convolutional neural network (MSCNN) that combines a gated recurrent unit and attention mechanism (GRU-AM) and a fusion with an autoregressive (AR) model. The vessel’s latitude and longitude information obtained by the HF radar is sent into the convolutional neural network (CNN) with different window lengths in parallel, and feature fusion is performed on the extracted multi-scale features. The deep GRU model is built to learn the time series with the GRU structure to preserve historical information. Different weights are given to the features using the temporal attention mechanism (AM), which helps the network learn the key information. The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. To make full use of the HF radar tracking information, the broken trajectory prediction is carried out by forward and backward computation using data from before and after the fracture, respectively. Weights are then assigned to the two predicted results by the entropy-value method to obtain the final ship trajectory by weighted summation. Field experiments show that the proposed method can accurately forecast the trajectories of vessels concealed in clutter. In comparison with other mainstream methods, the new method performs better in estimation accuracy for HF radar vessels concealed in clutter.

Research on language analysis of English translation system based on fuzzy algorithm

2020 ◽  
pp. 1-9
Author(s):  
Zhiqian Yuan ◽  
Chaoyang Jin ◽  
Zhaojun Chen
Keyword(s):  
Image Recognition ◽  
English Translation ◽  
Rapid Development ◽  
Language Translation ◽  
Translation System ◽  
Fuzzy Algorithm ◽  
Language Analysis ◽  
Gaussian Blur ◽  
Language Characteristics ◽  
Translation Accuracy

In recent years, with the rapid development of computer technology, the need for barrier-free communication between people of all countries have become more and more urgent. Therefore, it is extremely important to establish a high translation accuracy and high-quality English translation system. At present, Although the various English translation systems on the market have solved the communication problems between different languages to a certain extent, there are a series of problems such as language translation ambiguity and inaccurate use of words in translation methods, in order to improve English The translation accuracy of the translation system can improve the quality of the English translation system. This paper proposes a language analysis study of the English translation system based on fuzzy algorithms. The research of this paper firmly grasps the analysis and understanding of the language, analyzes it from the corpus, vocabulary, syntax, and translation characteristics, and fully understands its language characteristics, so as to eliminate the semantic understanding ambiguity in the translation process to a certain extent. Thereby improving the accuracy of the translation. The English translation system designed in this paper includes an image input module and an image recognition module, so the Gaussian blur algorithm is used for processing. The Gaussian blur algorithm can retain edge information in the edge area where the pixel value of the image changes sharply, and can effectively remove noise and enhance the image effect. Therefore, this article uses fuzzy algorithm-based English translation system language analysis research, first analyze the English language characteristics, and then use Gaussian fuzzy algorithm to denoise the image in the translation system, and then display the image recognition results.

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

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