scholarly journals Automation of solving planimetry problems written in Ukrainian

2020 ◽  
pp. 071-080
Author(s):  
O.P. Zhezherun ◽  
◽  
O.R. Smysh ◽  
◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Comprehensive Analysis ◽  
Text Representation ◽  
Natural Languages ◽  
Different Types ◽  
Mathematical Problems ◽  
Further Development

The article focuses on developing a software solution for solving planimetry problems that are written in Ukrainian. We discuss tendencies and available abilities in Ukrainian natural language processing. Presenting a comprehensive analysis of different types of describing a problem, which shows regularities in the formulation and structure of the text representation of problems. Also, we demonstrate the similarities of writing a problem not only in Ukrainian but also in Belarusian, English, and Russian languages. The final result of the paper is a system that uses the morphosyntactic analyzer to process a problem’s text and provide the answer to it. Ukrainian natural language processing is growing rapidly and showing impressive results. Huge possibilities appear as the Gold standard annotated corpus for Ukrainian language was recently developed. The created architecture is flexible, which indicates the possibility of adding both new geometry figures and their properties, as well as the additional logic to the program. The developed system with a little reformatting can be used with other natural languages, such as English, Belarusian or Russian, as the algorithm for text processing is universal due to the globally accepted representations for presenting such types of mathematical problems. Therefore, the further development of the system is possible.

scholarly journals Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

2019 ◽  
Vol 48 (3) ◽  
pp. 432-445 ◽  
Author(s):  
Laszlo Toth ◽  
Laszlo Vidacs
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Software Engineering ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Software Systems ◽  
Functional Requirements ◽  
Natural Languages ◽  
System Analyst

Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

scholarly journals Natural Language Processing by Enhanced Honey Encryption Technique

2019 ◽  
Vol 8 (12S) ◽  
pp. 159-163
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cyber Attacks ◽  
Binary Form ◽  
Brute Force ◽  
Natural Languages ◽  
Cipher Text ◽  
The Right ◽  
Binary Strings

Traditional encryption systems and techniques have always been vulnerable to brute force cyber-attacks. This is due to bytes encoding of characters utf8 also known as ASCII characters. Therefore, an opponent who intercepts a cipher text and attempts to decrypt the signal by applying brute force with a faulty pass key can detect some of the decrypted signals by employing a mixture of symbols that are not uniformly dispersed and contain no meaningful significance. Honey encoding technique is suggested to curb this classical authentication weakness by developing cipher-texts that provide correct and evenly dispersed but untrue plaintexts after decryption with a false key. This technique is only suitable for passkeys and PINs. Its adjustment in order to promote the encoding of the texts of natural languages such as electronic mails, records generated by man, still remained an open-end drawback. Prevailing proposed schemes to expand the encryption of natural language messages schedule exposes fragments of the plaintext embedded with coded data, thus they are more prone to cipher text attacks. In this paper, amending honey encoded system is proposed to promote natural language message encryption. The main aim was to create a framework that would encrypt a signal fully in binary form. As an end result, most binary strings semantically generate the right texts to trick an opponent who tries to decipher an error key in the cipher text. The security of the suggested system is assessed..

Advances in Computational Linguistics and Text Processing Frameworks

2020 ◽  
pp. 217-244
Author(s):  
Ayush Srivastav ◽  
Hera Khan ◽  
Amit Kumar Mishra
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity ◽  
Part Of Speech

The chapter provides an eloquent account of the major methodologies and advances in the field of Natural Language Processing. The most popular models that have been used over time for the task of Natural Language Processing have been discussed along with their applications in their specific tasks. The chapter begins with the fundamental concepts of regex and tokenization. It provides an insight to text preprocessing and its methodologies such as Stemming and Lemmatization, Stop Word Removal, followed by Part-of-Speech tagging and Named Entity Recognition. Further, this chapter elaborates the concept of Word Embedding, its various types, and some common frameworks such as word2vec, GloVe, and fastText. A brief description of classification algorithms used in Natural Language Processing is provided next, followed by Neural Networks and its advanced forms such as Recursive Neural Networks and Seq2seq models that are used in Computational Linguistics. A brief description of chatbots and Memory Networks concludes the chapter.

Machine Learning in Natural Language Processing

2010 ◽  
pp. 302-324
Author(s):  
Marina Sokolova ◽  
Stan Szpakowicz
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Word Sense Disambiguation ◽  
Machine Learning Techniques ◽  
Word Sense ◽  
Part Of Speech ◽  
Applications Of Machine Learning

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

Application of Natural Language Processing for Information Retrieval

2013 ◽  
Vol 380-384 ◽  
pp. 2614-2618 ◽  
Author(s):  
Su Mei Xi
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Comprehensive Analysis

Through a comprehensive analysis of using natural language processing in information retrieval, we compared the effects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks of more suitable as well as accurate results of natural language processing.

scholarly journals ELEKTRONICZNY KORPUS TEKSTÓW POLSKICH Z XVII I XVIII W. – PROBLEMY TEORETYCZNE I WARSZTATOWE

2020 ◽  
pp. 32-51
Author(s):  
Włodzimierz Gruszczyński ◽  
Dorota Adamiec ◽  
Renata Bronikowska ◽  
Aleksandra Wieczorek
Keyword(s):  
Natural Language Processing ◽  
Foreign Language ◽  
Natural Language ◽  
Language Processing ◽  
18Th Century ◽  
Text Representation ◽  
Grammatical Structure ◽  
Historical Material ◽  
Polish Language ◽  
National Corpus

Electronic Corpus of 17th- and 18th-century Polish Texts – theoretical and workshop problems Summary This paper presents the Electronic Corpus of 17th- and 18th-century Polish Texts (KorBa) – a large (13.5-million), annotated historical corpus available online. Its creation was modelled on the assumptions of the National Corpus of Polish (NKJP), yet the specifi c nature of the historical material enforced certain modifi cations of the solutions applied in NKJP, e.g. two forms of text representation (transliteration and transcription) were introduced, the principle of designating foreign-language fragments was adopted, and the tagset was adapted to the description of the grammatical structure of the Middle Polish language. The texts collected in KorBa are diversified in chronological, geographical, stylistic, and thematic terms although, due to e.g. limited access to the material, the postulate of representativeness and sustainability of the corpus was not fully implemented. The work on the corpus was to a large extent automated as a result of using natural language processing tools. Keywords: electronic text corpus – historical corpus – 17th-18th-century Polish – natural language processing

scholarly journals NATURAL LANGUAGE PROCESSING AND BIM IN AECO SECTOR: A STATE OF THE ART

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Giuseppe Martino Di Giuda ◽  
Mirko Locatelli ◽  
Elena Seghezzi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Structured Data ◽  
Information Modeling ◽  
Construction Process ◽  
Management Techniques ◽  
Design Requirements ◽  
Further Development

The research provides state-of-the-art theories, methods, and applications of Natural Language Processing in a BIM approach to define advantages, weaknesses, and potential developments. Traditionally, the design and construction process requirements are expressed through verbal qualitative evaluations instead of using numerical and structured data. Information modeling and management methods can hardly manage requirements expressed by means of qualitative and unstructured expressions. This paper aims to define the state of the art on Natural Language Processing applications for the numerical translation of basic design requirements in the AECO sector. The major advantages of this research would be the optimization and automation of numerical definition and validation of requirements in a structured data form. Structured data can, in fact, be easily managed in a BIM approach. NLP supporting information modeling methods allows the application of requirements engineering and management techniques to handle construction processes from a data-driven perspective. An investigation of the setting of a decision support system, based on NLP, for the definition and validation of requirements in the early stages of the construction process, is also provided as a potential further development.

Sign in / Sign up

Export Citation Format

Share Document