English spoken stress recognition based on natural language processing and endpoint detection algorithm

2021 ◽  
pp. 002072092098353
Author(s):  
Tieyan Yue
Keyword(s):  
Fractal Dimension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Detection Algorithm ◽  
Endpoint Detection ◽  
Time Frequency ◽  
Language System ◽  
Computer Aided ◽  
Speech Feature

Nowadays, there are more and more researches on the application of natural language processing technology in computer-aided language system, which can provide a good assistant role for foreign language learners. However, in the research of computer-aided language system, there are still some deficiencies in the recognition of English spoken stress nodes, which cannot be well recognized. Based on this, this paper proposes a method of English spoken accent recognition based on natural language processing and endpoint detection algorithm, which aims to promote the accuracy of accent recognition in the computer-aided language system and improve the performance of the computer-aided language system. In order to avoid the interference of background noise, this paper proposes a short-term time-frequency endpoint detection algorithm which can accurately judge the beginning and end of speech in complex environment. Then, on the basis of traditional speech feature extraction and fractal dimension theory, a nonlinear fractal dimension speech feature is extracted. Finally, RankNet is used to process the extracted features to realize the recognition of English spoken stress nodes. In the simulation analysis, the application effect of the short-term time-frequency endpoint detection algorithm proposed in this paper in the complex background noise and the effect of non-linear fractal dimension speech features on the recognition of English spoken stress nodes are verified. Finally, the performance and good application effect of the method designed in this paper are illustrated.

scholarly journals Clinical trial cohort selection based on multi-level rule-based natural language processing system

2019 ◽  
Vol 26 (11) ◽  
pp. 1218-1226 ◽  
Author(s):  
Long Chen ◽  
Yu Gu ◽  
Xin Ji ◽  
Chao Lou ◽  
Zhiyong Sun ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Rule Based ◽  
Language System ◽  
Unified Medical Language System ◽  
Rule Based System ◽  
Medical Language ◽  
Cohort Selection

Abstract Objective Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients’ eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. Materials and Methods The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. Results and Discussion The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors’ rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn’t achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. Conclusion Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.

scholarly journals Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zihui Zheng
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Detection Algorithm ◽  
Processing Technology ◽  
Experimental Comparison ◽  
Intelligent Detection ◽  
Web Text Mining

With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.

scholarly journals Malicious URL Detection Algorithm Based on Multi Neural Network Series

CONVERTER ◽  
2021 ◽  
pp. 579-590
Author(s):  
Weirong Xiu
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Recurrent Neural Network ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Global Features ◽  
Multi Neural Network

Convolutional neural network based on attention mechanism and a bidirectional independent recurrent neural network tandem joint algorithm (CATIR) are proposed. In natural language processing related technologies, word vector features are extracted based on URLs, and the extracted URL information features and host information features are merged. The proposed CATIR algorithm uses CNN (Convolutional Neural Network) to obtain the deep local features in the data, uses the Attention mechanism to adjust the weights, and uses IndRNN (Independent Recurrent Neural Network) to obtain the global features in the data. The experimental results shows that the CATIR algorithm has significantly improved the accuracy of malicious URL detection based on traditional algorithms to 96.9%.

The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code

2019 ◽  
Vol 29 (1) ◽  
pp. 1388-1407 ◽  
Author(s):  
Ayad Tareq Imam ◽  
Ayman Jameel Alnsour
Keyword(s):  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Programming Language ◽  
Language Processing ◽  
Semantic Role Labeling ◽  
Code Generator ◽  
Computer Aided ◽  
Automatic Code ◽  
Computer Aided Software Engineering

Abstract Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

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