scholarly journals System analysis of the natural language modeling problem

2021 ◽  
Vol 12 (5-2021) ◽  
pp. 57-66
Author(s):  
Dzavdet Sh. Suleimanov ◽  
◽  
Alexander Ya. Fridman ◽  
Rinat A. Gilmullin ◽  
Boris A. Kulik ◽  
...  
Keyword(s):  
Natural Language ◽  
Programming Languages ◽  
System Analysis ◽  
Language Modeling ◽  
Root Cause ◽  
Modeling Problem ◽  
Modeling Systems ◽  
Technological Tools ◽  
Low Efficiency ◽  
Agglutinative Languages

System analysis of the problem of modeling a natural language (NL) made it possible to formulate the root cause of the low efficiency of modern means for accumulating and processing knowledge in such languages. This is the complexity of intellectualization for such tools, which are created on the basis of primitive artificial programming languages that practically represent a subset of flectional analytical languages or artificial constructions based on them. To reduce the severity of the identified problem, it is proposed to build NL modeling systems on the basis of technological tools for verbalization and recognition of sense. These tools consist of semiotic models of NL lexical and grammatical means. This approach seems to be especially promising for agglutinative languages; it is supposed to be implemented on the example of the Tatar language.

scholarly journals A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

2021 ◽  
Vol 11 (7) ◽  
pp. 3095
Author(s):  
Suhyune Son ◽  
Seonjeong Hwang ◽  
Sohyeun Bae ◽  
Soo Jun Park ◽  
Jang-Hwan Choi
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Empirical Investigation ◽  
Natural Language Understanding ◽  
Language Modeling ◽  
Language Understanding ◽  
Task Learning ◽  
Language Representation ◽  
Internal Transfer ◽  
Two Stages

Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU) tasks. However, one drawback is that confusion about the language representation of various tasks arises during the training of the MT-DNN model. Inspired by the internal-transfer weighting of MTL in medical imaging, we introduce a Sequential and Intensive Weighted Language Modeling (SIWLM) scheme. The SIWLM consists of two stages: (1) Sequential weighted learning (SWL), which trains a model to learn entire tasks sequentially and concentrically, and (2) Intensive weighted learning (IWL), which enables the model to focus on the central task. We apply this scheme to the MT-DNN model and call this model the MTDNN-SIWLM. Our model achieves higher performance than the existing reference algorithms on six out of the eight GLUE benchmark tasks. Moreover, our model outperforms MT-DNN by 0.77 on average on the overall task. Finally, we conducted a thorough empirical investigation to determine the optimal weight for each GLUE task.

scholarly journals Statistical Language Modeling for Automatic Speech Recognition of Agglutinative Languages

10.5772/6380 ◽  
2008 ◽  
Author(s):  
Ebru Arsoy ◽  
Mikko Kurimo ◽  
Murat Saralar ◽  
Teemu Hirsimki ◽  
Janne Pylkknen ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Language Modeling ◽  
Statistical Language Modeling ◽  
Agglutinative Languages

Design and Implementation a Testing Environment for Wireless Sensor Networks

2013 ◽  
Vol 712-715 ◽  
pp. 1923-1927
Author(s):  
Zhang Fan ◽  
Dong Yu Yang ◽  
Lin Jun ◽  
Chun Hui Yang
Keyword(s):  
Wireless Sensor Networks ◽  
System Analysis ◽  
Wireless Sensor ◽  
Testing System ◽  
Software Environment ◽  
Testing Environment ◽  
Current Testing ◽  
Design And Implementation ◽  
Service Oriented ◽  
Low Efficiency

This paper discusses the design, implementation and analysis of a system analysis for wireless sensor network. Current testing systems have low efficiency on software environment. We proposed a service oriented software approach, and gave out an experimental analysis of reliability. The testing system greatly decreases the development workload on server. Experiment reveals that the result of reliability analysis is accurate and the testing system is effective.

scholarly journals SISTEM CUSTOMER SERVICE PT KERETA API INDONESIA (PERSERO) BERBASIS CHATBOT MENGGUNAKAN KOMPUTASI BAHASA

2021 ◽  
Vol 5 (1) ◽  
pp. 37-41
Author(s):  
Nia Shafira ◽  
◽  
Etin Martiana ◽  
Rengga Asmara
Keyword(s):  
Human Resources ◽  
Natural Language ◽  
Customer Service ◽  
Customer Loyalty ◽  
Service Provider ◽  
Language Modeling ◽  
Service Performance ◽  
Service Workers ◽  
New Approach ◽  
Automatic Information

As the main train service provider company in Indonesia, PT Kereta Api Indonesia (PT KAI) has many customers who need information. In order to maintain customer loyalty, PT KAI must respond quickly and be adaptive to technology to provide the best service to customers. Limited human resources make PT KAI unable to serve customers simultaneously, so customers often have to wait for a response. In order to provide the best service, automatic messages are needed in order to help customer service performance respond quickly and at the same time with no cost, access anytime and anywhere. This study proposes a new approach with chatbots as a medium for conveying automatic information quickly and simultaneously. This chatbot is made with a computational language that focuses on natural language modeling and cosine similarity as a method for calculating the proximity of inputs and databases. This research can help PT KAI's customer service workers to answer customer needs automatically.

Knowledge-Based Task Planning Using Natural Language Processing for Robotic Manufacturing

2010 ◽  
Author(s):  
Iraj Mantegh ◽  
Nazanin S. Darbandi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Programming Languages ◽  
Language Processing ◽  
New Method ◽  
Robot Programming ◽  
Task Planning ◽  
End User ◽  
Knowledge Based ◽  
Manufacturing Applications

Robotic alternative to many manual operations falls short in application due to the difficulties in capturing the manual skill of an expert operator. One of the main problems to be solved if robots are to become flexible enough for various manufacturing needs is that of end-user programming. An end-user with little or no technical expertise in robotics area needs to be able to efficiently communicate its manufacturing task to the robot. This paper proposes a new method for robot task planning using some concepts of Artificial Intelligence. Our method is based on a hierarchical knowledge representation and propositional logic, which allows an expert user to incrementally integrate process and geometric parameters with the robot commands. The objective is to provide an intelligent and programmable agent such as a robot with a knowledge base about the attributes of human behaviors in order to facilitate the commanding process. The focus of this work is on robot programming for manufacturing applications. Industrial manipulators work with low level programming languages. This work presents a new method based on Natural Language Processing (NLP) that allows a user to generate robot programs using natural language lexicon and task information. This will enable a manufacturing operator (for example for painting) who may be unfamiliar with robot programming to easily employ the agent for the manufacturing tasks.

scholarly journals Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3258 ◽  
Author(s):  
Bai ◽  
Sun ◽  
Zang ◽  
Zhang ◽  
Shen ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Event Identification ◽  
Short Period ◽  
Grid Monitoring ◽  
Low Efficiency ◽  
Event Based ◽  
Power Dispatching

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.

Sign in / Sign up

Export Citation Format

Share Document