scholarly journals Ontological approach to Chinese text processing

Doklady BGUIR ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 49-56
Author(s):  
Q. Longwei
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Evaluation System ◽  
Chinese Language ◽  
Knowledge Bases ◽  
Careful Consideration ◽  
Semantic Model ◽  
Ontological Approach

To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

Intelligent Support for Building Knowledge Bases for Natural Language Processing

2007 ◽  
pp. 86-113 ◽  
Author(s):  
Son B. Pham ◽  
Achim Hoffmann
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Current Knowledge ◽  
Knowledge Bases ◽  
Intelligent Support ◽  
Current Knowledge Base ◽  
Complex Knowledge ◽  
Comparable Quality

In this chapter we discuss ways of assisting experts to develop complex knowledge bases for a variety of natural language processing tasks. The proposed techniques are embedded into an existing knowledge acquisition framework, KAFTIE, specifically designed for building knowledge bases for natural language processing. Our intelligent agent, the rule suggestion module within KAFTIE, assists the expert by suggesting new rules in order to address incorrect behavior of the current knowledge base. The suggested rules are based on previously entered rules which were “hand-crafted” by the expert. Initial experiments with the new rule suggestion module are very encouraging as they resulted in a more compact knowledge base of comparable quality to a fully hand-crafted knowledge base. At the same time the development time for the more compact knowledge base was considerably reduced.

A WORD-BASED CHINESE LANGUAGE UNDERSTANDING SYSTEM

1988 ◽  
Vol 02 (01) ◽  
pp. 25-35
Author(s):  
TIAN-SHUN YAO
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Chinese Language ◽  
Computer Programs ◽  
World Knowledge ◽  
Knowledge Source ◽  
Language Understanding ◽  
Language Analysis ◽  
The World

With the word-based theory of natural language processing, a word-based Chinese language understanding system has been developed. In the light of psychological language analysis and the features of the Chinese language, this theory of natural language processing is presented with the description of the computer programs based on it. The heart of the system is to define a Total Information Dictionary and the World Knowledge Source used in the system. The purpose of this research is to develop a system which can understand not only Chinese sentences but also the whole text.

scholarly journals System of classification for personnel selection based upon Ukrainian language analyze

2020 ◽  
pp. 034-040
Author(s):  
O.P. Zhezherun ◽  
◽  
M.S. Ryepkin ◽  
◽  
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Classification System ◽  
Personnel Selection ◽  
Classification Systems ◽  
System Expansion ◽  
Recruitment Process

The article describes a classification system with natural language processing. Many systems use neural networks, but it needs massive amounts of data for training, which is not always available. Authors propose to use ontologies in such systems. As example of such approach it is shown the classification system, which helps to form a list of the best candidates during the recruitment process. An overview of the methods for ontologies constructing and language analyzers appropriate for classification systems are presented. The system in the form of a knowledge base is constracted. Described system supports Ukrainian and English languages. The possible ways of system expansion is regarded.

scholarly journals Semantic Model Evaluation Dataset For Indonesian In Al-Qur'an Vocabulary: Similarity and Relatedness

2020 ◽  
Vol 7 (1) ◽  
pp. 54-60
Author(s):  
Falia Amalia ◽  
Moch Arif Bijaksana
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Summarization ◽  
Future Research ◽  
Semantic Model ◽  
Vector Method ◽  
Spearman Rank ◽  
Correlation Result ◽  
Evaluation Dataset

Abstract — The Qur'an is one of the research in linguistic branches that have not been studied by many experts in their field so it has not gotten a popular place. Whereas in the Qur'an, very many words can be used to be researched especially in terms of Natural Language Processing such as text classification, document clustering, text summarization, etc. One of them is like the semantic similarity and the Distribution Semantic Model. The purpose of this writing is to try to create an evaluation dataset in the model of semantic distribution in Bahasa Indonesia with two classes of words that are noun and verb, looking for equal value and linkage of 500 word-pairs provided. Hopefully by looking at this, the semantic sciences that exist for the study of the Qur'an are growing, especially in the translation of the Quran in the Indonesia Language. This research was created at the same time to create datasets such as previously conducted research, in order to hope that future research with the focus of other discussions can use this dataset to help with the research. The study uses 6236 number of verses and from the number of such verses, the system gets 2193 for nouns and 1733 for verbs. The amount is processed using the Sim-rail vector method, a questionnaire against 15 respondents and gold standard, to get the performance value measured using Spearman Rank and get a correlation result of 0.909. Keywords — Natural Language Processing; Distribution Semantic Model; Sim-Rel Vector; Spearman Rank

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 Coreference resolution Ontology-based in sentiment analysis

2019 ◽  
Vol 20 (K9) ◽  
pp. 23-30
Author(s):  
Le Thi Thuy ◽  
Phan Thi Tuoi ◽  
Quan Thanh Tho
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Evaluation Methods ◽  
English Text ◽  
Coreference Resolution ◽  
Research Topics ◽  
Reference Resolution

Entity co-reference resolution and sentiment analysis are independent problems and popular research topics in the community of natural language processing. However, the combination of those two problems has not been getting much attention. Thus, this paper susgests to apply knowledge base to solve co- reference between object and aspect with sentiment. In addition, the paper also proposes the model of Ontology-based co-reference resolution in sentiment analysis for English text. Finally, we also discuss evaluation methods applied for our model and the results obtained.

Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System

2019 ◽  
Vol 17 (1) ◽  
pp. 89-97
Author(s):  
Qiao Li ◽  
Junming Liu
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Knowledge Base ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Discovery System

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.

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