An approach of intelligent searching of information in texts
Paper proposes an approach aimed at question oriented searching of information in texts. Texts are parsed, keywords and extra features of questions are marked, and sentences in text with the most relevant information to question are defined. Proposed approach is applied to Cyrillic and Latin languag...
Збережено в:
| Дата: | 2023 |
|---|---|
| Автор: | |
| Формат: | Стаття |
| Мова: | English |
| Опубліковано: |
Інститут програмних систем НАН України
2023
|
| Теми: | |
| Онлайн доступ: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/529 |
| Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
| Назва журналу: | Problems in programming |
| Завантажити файл: |  |
Репозитарії
Problems in programming| id |
pp_isofts_kiev_ua-article-529 |
|---|---|
| record_format |
ojs |
| resource_txt_mv |
ppisoftskievua/f3/af6b740552451636187cf1254f8957f3.pdf |
| spelling |
pp_isofts_kiev_ua-article-5292023-06-25T07:02:24Z An approach of intelligent searching of information in texts Методика аналітичного пошуку інформації у текстах Chebanuyk, O.V. text processing on different languages; grammar rules; semantic analysis; software system for text analysis; software architecture UDC 004.415.2.045 (076.5) обробка текстів різними мовами; правила граматики; семантичний аналіз; програмна система аналізу тескстів; аріхтектура програмного забезпечення УДК 004.415.2.045 (076.5) Paper proposes an approach aimed at question oriented searching of information in texts. Texts are parsed, keywords and extra features of questions are marked, and sentences in text with the most relevant information to question are defined. Proposed approach is applied to Cyrillic and Latin languages.Case study illustrates how to obtain answers to questions about Bulgarian fairytale that is represented on different languages (Bulgarian and English). Evaluation of the proposed approach is introduced. Description of the software architecture and source code of the corresponding software system are represented. Data structures and examples of *.xml files for storing information about question and answers are outlined.Prombles in programming 2022; 3-4: 281-288 У роботі представлено методику пошуку інформації в текстах на запит. Тексти аналізуються, визначаються ключові слова та додаткові характеристики запитань, потім, у тексті шукаються речення з найбільш релевантною інформацією щодо запитання. Запропонована матодика застосовується до мов, що використовують як кирилицю так і латиницю. У прикладі показано, як отримати відповіді на запитання про болгарську казку, яка представлена різними мовами (болгарською та англійською). У роботі представлено оцінювання запропонованого підходу. Представлено опис архітектури програмного забезпечення та вихідного коду відповідної програмної системи. Описано структури даних та приклади *.xml файлів для зберігання інформації про запитання та відповіді.Prombles in programming 2022; 3-4: 281-288 Інститут програмних систем НАН України 2023-01-23 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/529 10.15407/pp2022.03-04.281 PROBLEMS IN PROGRAMMING; No 3-4 (2022); 281-288 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 3-4 (2022); 281-288 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 3-4 (2022); 281-288 1727-4907 10.15407/pp2022.03-04 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/529/581 Copyright (c) 2023 PROBLEMS IN PROGRAMMING |
| institution |
Problems in programming |
| baseUrl_str |
https://pp.isofts.kiev.ua/index.php/ojs1/oai |
| datestamp_date |
2023-06-25T07:02:24Z |
| collection |
OJS |
| language |
English |
| topic |
text processing on different languages grammar rules semantic analysis software system for text analysis software architecture UDC 004.415.2.045 (076.5) |
| spellingShingle |
text processing on different languages grammar rules semantic analysis software system for text analysis software architecture UDC 004.415.2.045 (076.5) Chebanuyk, O.V. An approach of intelligent searching of information in texts |
| topic_facet |
text processing on different languages grammar rules semantic analysis software system for text analysis software architecture UDC 004.415.2.045 (076.5) обробка текстів різними мовами правила граматики семантичний аналіз програмна система аналізу тескстів аріхтектура програмного забезпечення УДК 004.415.2.045 (076.5) |
| format |
Article |
| author |
Chebanuyk, O.V. |
| author_facet |
Chebanuyk, O.V. |
| author_sort |
Chebanuyk, O.V. |
| title |
An approach of intelligent searching of information in texts |
| title_short |
An approach of intelligent searching of information in texts |
| title_full |
An approach of intelligent searching of information in texts |
| title_fullStr |
An approach of intelligent searching of information in texts |
| title_full_unstemmed |
An approach of intelligent searching of information in texts |
| title_sort |
approach of intelligent searching of information in texts |
| title_alt |
Методика аналітичного пошуку інформації у текстах |
| description |
Paper proposes an approach aimed at question oriented searching of information in texts. Texts are parsed, keywords and extra features of questions are marked, and sentences in text with the most relevant information to question are defined. Proposed approach is applied to Cyrillic and Latin languages.Case study illustrates how to obtain answers to questions about Bulgarian fairytale that is represented on different languages (Bulgarian and English). Evaluation of the proposed approach is introduced. Description of the software architecture and source code of the corresponding software system are represented. Data structures and examples of *.xml files for storing information about question and answers are outlined.Prombles in programming 2022; 3-4: 281-288 |
| publisher |
Інститут програмних систем НАН України |
| publishDate |
2023 |
| url |
https://pp.isofts.kiev.ua/index.php/ojs1/article/view/529 |
| work_keys_str_mv |
AT chebanuykov anapproachofintelligentsearchingofinformationintexts AT chebanuykov metodikaanalítičnogopošukuínformacííutekstah AT chebanuykov approachofintelligentsearchingofinformationintexts |
| first_indexed |
2024-09-12T19:29:46Z |
| last_indexed |
2024-09-12T19:29:46Z |
| _version_ |
1815407539534168064 |
| fulltext |
281
Програмні засоби штучного інтелекту
UDC 004.415.2.045 (076.5) https://doi.org/10.15407/pp2022.03-04.281
AN APPROACH OF INTELLIGENT SEARCHING
OF INFORMATION IN TEXTS
Olena Chebanyuk
Paper proposes an approach aimed at question oriented searching of information in texts. Texts are parsed, keywords and extra
features of questions are marked, and sentences in text with the most relevant information to question are defined. Proposed
approach is applied to Cyrillic and Latin languages.
Case study illustrates how to obtain answers to questions about Bulgarian fairytale that is represented on different languages
(Bulgarian and English). Evaluation of the proposed approach is introduced. Description of the software architecture and source
code of the corresponding software system are represented. Data structures and examples of *.xml files for storing information
about question and answers are outlined.
Keywords: Text Processing on Different Languages, Grammar Rules, Semantic Analysis, Software System for Text Analysis,
Software Architecture,
У роботі представлено методику пошуку інформації в текстах на запит. Тексти аналізуються, визначаються ключові слова та
додаткові характеристики запитань, потім, у тексті шукаються речення з найбільш релевантною інформацією щодо запитан-
ня. Запропонована матодика застосовується до мов, що використовують як кирилицю так і латиницю. У прикладі показано,
як отримати відповіді на запитання про болгарську казку, яка представлена різними мовами (болгарською та англійською).
У роботі представлено оцінювання запропонованого підходу. Представлено опис архітектури програмного забезпечення та
вихідного коду відповідної програмної системи. Описано структури даних та приклади *.xml файлів для зберігання інфор-
мації про запитання та відповіді.
Ключові слова: обробка текстів різними мовами, правила граматики, семантичний аналіз, програмна система аналізу теск-
стів, аріхтектура програмного забезпечення.
Introduction
Nowadays values of information in digital world are increased. In order to perform effective search user
must proceed a large amount of data. In order to take this process more effective different software systems for
searching and processing of information are used. Existing systems have the next difficulties to use: systems are not
flexible (based on artificial networks, based on ontologies), expensive, difficult to changes, difficult to be adopted
to different languages or environments. From the other side the designing, development, and supporting of such
systems requires a lot of efforts to adopt software system for different purposes.
Solution may be in the area – flexible systems, open for other languages, allowing to filter user requests and
reduce the number of searched information.
Literature review
Paper [1] represents a software system is used to determine the degree of semantic similarity of two short
texts written in Serbian. An approach allowing to perform Semantic Similarity of Short Texts in Languages. This
approach is consists from the next steps:
Corpus acquisition deals with finding a sufficiently large set of texts that could be used to generate a se-
mantic space.
Corpus parsing is used to remove any superfluous information from further consideration. (for example
specific XML tags or other, irrelevant data.)
Corpus preprocessing serves to reduce the amount of different words in the corpus, effectively reducing the
context vector dimension:
1. Text cleaning – this includes the deletion of all text characters not belonging to the native script
of the language in question, the removal of numbers and words that contain numbers, the elimination of
punctuation marks and the shifting of all capital letters into lower case [1].
2. Stop-words removal – stop-words are auxiliary words like prepositions, pronouns, interjections and
conjunctions, which carry negligible semantic information, but which are often encountered due to their language
function. By removing those words, we decrease the total number of different words in the corpus [1].
The result is that the semantic space is reduced and the accuracy of the semantic algorithms is increased,
since the links between semantically important words become more emphasized. The stop-word was formed by
gathering the most frequent words from the text corpus. (General knowledge were taken from an encyclopedia).
The information about word frequencies in the corpus which is gathered in this step is saved for later use in cal-
culating various term frequencies (TFs) for each word [1].
3. Stemming – (solving coding problems, for example comparing UTF-8 format and is written partially
in Cyrillic and partially in Latin alphabet or ASCII coding system. In order to preserve compatibility with the
stemmer module, special coding system was designed [1].
© О.В. Чебанюк, 2022
ISSN 1727-4907. Проблеми програмування. 2022. № 3-4. Спеціальний випуск
282
Програмні засоби штучного інтелекту
Choosing an algorithm for the creation of the semantic space and supplying it with the preprocessed
corpus text.
The reduction of context vector dimension. Each algorithm has its own post-processing routine which is
encapsulated within the algorithm, as defined in the S-Space package [1].
Paper [2] presents the approach of defining entities in court decisions. It is proposed to prepare courts’ deci-
sions in the special structure of documents in order to simplify search procedure. Also classification and advantages
and drawbacks of different software systems for semantic analysis is represented. Preparing unified structure of a docu-
ment simplifies search procedure, but requires additional efforts for preparing of document in a specific view.
Approaches devoted to analysis of object state in decision support systems allows to analyze different
states of objects (and as a conclusion – characteristics of entities) with the aim to extract metainformation about
entities and get the answers to questions in the text. Such approaches may be implemented if there is an informa-
tion that state of object or domain entities may be changed during story [3].
Other implementation of question-oriented analysis of texts is to get questions essential for specific group
of users to predict their relation, emotions, and opinions about texts. Typical question may be applied to many dif-
ferent texts with the aim to select the best test considering some conditions of chosen social group. For example
searching the most appropriate texts fro children [4].
Conclusion from the review and challenges for the approach
After analyzing the existing solutions for processing of texts, the following criteria for modern expert
system aimed to process texts were defined:
1. Support of the functionality of processing information from various sources and preparing reports.
1.1. Speech to text input;
1.2. Processing text in different formats and encodings;
1.3. Recognizing texts from images.
2. Search the exact answer to the question.
2.1. Processing text with some grammar mistakes;
2.2. Processing texts containing smiles or special symbols;
2. Usability the system should be easy to use and find the answer to the question.
4. Convenient representation of answers.
4.1. Processing answers in text to speech modules
4.2. Representing answers in different languages
Proposed approach
Parse the question
Find keywords in questions
Define metainformation related to answers in text.
Search sentences that correspond to defined metainformation and represent them to user.
Metainformation about keywords for different types of questions
The list of the proposed characteristics for questions is given below. Aim of this classification is to take a
marks in text according to types of questions.
Table 1. List of metainformation that is extracted from questions
Metainformation
for answers in questions
Question words
in Bulgarian
Question words
in English
(1) alive entities кой? who?
(2) entities of place from domain.
They are defined by special prepositions in text къде? where?
(3) entities of time кога? when?
(4) not alive entities какво е това? what is this?
(5) all entities that have numeric precision колко? how many(much)?)
(6) all event entities как? How?
Metainformation about features of answers extracted from text
It is proposed to classify domain entities according to defined characteristics. One entity may correspond to
several classes.
283
Програмні засоби штучного інтелекту
Table 2. List of metainformation that is extracted from texts
Description
of metainformation
Examples
for English language
Examples
for Bulgarian language
(1) All alive entities from do-
main description Defined by domain experts
(2) Place entities
At the
in the,
near,
far from,
under,
above
на,
в(във),
близо,
далеко,
под,
над
(3) Time entities
parts of the day (day, night, eve-
ning, morning, at __ o’clock)
parts of year (mounts)
Части на денонощието
(ден, нощ, сутрин, в __ часа)
части на годината (месеци)
(4) Not alive entities All domain entities that
are leaved after performing alive entities
(5) number entities
all entities that have numerical
definition before them
15 cats 15 котки
(6) Event entities
Quick,
easy,
hard,
in the middle
Бързо,
лестно,
трудно,
посреда
Case study
Consider a part of Bulgarian fairytale about three apples.
Table 3. Text for case study
Fairytale in Bulgarian Bulgarian Fairytale in English
Една жена имала трима синове.
В градината на къщата им растяло чудно красиво
ябълково дърво.
Всяка година то раждало само по една ябълка, но
не каква да е, а златна.
Ала в нощта, когато ябълката узрявала и така забле-
стявала между клоните, че цялата градина грейва-
ла, долитала една хала и откъсвала златната ябълка.
Една година, щом дошло време ябълката да узрее,
най-големият син рекъл на майка си:
— Мале, ще отида да вардя ябълката.
Дай ми един нож и орехи, та да не заспя.
Седнал най-големият син под ябълката и започнал
да троши орехи.
Изведнъж задухал силен вятър и дърветата се пре-
вили чак до земята.
Тъмен облак закрил луната и звездите, а халата
слязла от небето, грабнала златната ябълка и докато
големият син се усети, отлетяла.
На другата година средният син казал на майка си:
— Мале, отивам да вардя ябълката. Дай ми един
нож и орехи, че тръгвам.
Седнал той под ябълката, ала се улисал да троши
и да яде орехи и така и не разбрал как халата откъ-
снала златната ябълка и изчезнала с нея.
A woman had three sons.
In the garden of their house grew a wonderfully beautiful
apple tree.
Every year it bore only one apple, but not just any apple,
but a golden one.
But on the night when the apple ripened and shone so
brightly between the branches that the whole garden
glowed, a challah flew by and plucked the golden apple.
One year, when the time came for the apple to ripen, the
eldest son said to his mother:
«Mom, I’m going to take the apple.» Give me a knife and
walnuts so I don’t fall asleep.
The eldest son sat under the apple and began to crush wal-
nuts.
Suddenly, a strong wind blew and the trees were bent all
the way to the ground.
A dark cloud covered the moon and the stars, came down
from heaven, grabbed the golden apple and before the el-
dest son felt it, flew away.
The next year the middle son said to his mother:
«Mom, I’m going to boil the apple.» Give me a knife and
walnuts, that I’m leaving.
He sat under the apple, but he enjoyed crushing and eating
walnuts and never understood how the robe tore off the
golden apple and disappeared with it.
Text is taken from
https://bulgarianhistory.org/trimata-bratq-i-zlatnata-qbalka/
284
Програмні засоби штучного інтелекту
Table 4. Analysis of text for case study
Domain entities
Ябълка,
хала,
орехи,
средният син,
най-големият син,
майка,
нож.
Apple,
challah,
walnuts,
middle son,
eldest son,
mother,
knife
Statistical characteristics of the text
Words – 173
Total value of the text - 990
Words – 205
Total value of the text - 1066
The next activity – is to take question about text. Matched metainformation about questions and answers are
marked by different colors (the same color for the same type of question).
Table with answers is given below.
Table 5. Questions and answers (entities of time)
Question related to entities of time
Кога е узряла ябълката? When the apple was riped?
Possible variants of answers
Ала в нощта
На другата година средният син казал на майка си:
But on the night
The next year the middle son said to his mother:
Statistical values
Words – 12
Total value of text - 60
Words – 14
Total value of text - 64
Кога най-големият син е чакал ябълката? When did the eldest son wait for the apple?
Possible variants of answers
Ала в нощта
На другата година средният син казал на майка си:
But on the night
The next year the middle son said to his mother:
Statistical values
Words – 12
Total value of text - 60
Words – 14
Total value of text - 64
Table 6. Questions and answers (alive entities)
Question related to alive entities
Кой е откраднал ябълката? Who stole the apple?
Possible variants of answers (all sentences with alive entities are considered)
долитала една хала и откъсвала златната ябълка
най-големият син рекъл на майка си:
Седнал най-големият син под ябълката и започнал
да троши орехи.
На другата година средният син казал на майка си:
ала се улисал да троши и да яде орехи и така и не
разбрал как халата откъснала златната ябълка и из-
чезнала с нея.
a challah flew by and plucked the golden apple
the eldest son said to his mother:
The eldest son sat under the apple
and began to crush walnuts.
The next year the middle son said to his mother:
but he delighted in crushing and eating walnuts and never
understood how the robe tore off the golden apple and dis-
appeared with it.
Statistical values
Words – 55
Total value of text - 306
Words – 61
Total value of text - 266
Other Question
Какво е ял най-големият син? What the eldest son eat?
Няма как да се намери отговор
в текста по този метод
There is no way to find an answer in the text
using this method
285
Програмні засоби штучного інтелекту
Designing of software architecture
Component diagram of the proposed software system is represented in the Figure 1. It illustrates the structure
of the system and interaction between its components.
Metainformation about questions and answers for every language is stored in special XML file. Then, it is nec-
essary to parse texts and questions. Information, extracted from XML parser, is transmitted to text parser and module
for the questions processing (Use Input processing module in the Figure 1.).
After parsing texts answers according to the type of question are obtained. After parsing questions keywords
are extracted (Table 2). After matching questions and information extracted from texts answers are proposed to user.
Metainforma�on
Ques�on
metadata
Answers
XML parser
Text parser User input
processing
Matching
tool
Figure 1. Component diagram of the software system “Intelligent search in texts’ ”
Preparing of metainformation for text analysis
In order to represent connection between metainformation in texts and questions part of XML file is proposed.
Structure of the file shows that in order to add new features of defining new answers in text it is necessary to modify
items of list <MetaList> (See Table 1 and Table 2). Also it is easy to add or remove new types of question.
<Bulgarian>
<TypeQ> къде? </TypeQ>
<MetaList>
<MetaText> на </MetaText>
<MetaText> в </MetaText>
<MetaText> във </MetaText>
<MetaText> близо </MetaText>
<MetaText> далеко </MetaText>
<MetaText> под </MetaText>
<MetaText> над </MetaText>
</MetaList>
286
Програмні засоби штучного інтелекту
<TypeQ> кога? </TypeQ>
<MetaList>
<MetaText> в нощта </MetaText>
<MetaText> нощ </MetaText>
<MetaText> деня </MetaText>
<MetaText> денят </MetaText>
<MetaText> сутрин </MetaText>
<MetaText> сутринта </MetaText>
<MetaText> днес </MetaText>
<MetaText> сега </MetaText>
<MetaText> никога </MetaText>
</MetaList>
</Bulgarian>
Description of software system realization
In order to realize such a software system it is necessary to solve the next task
Select data structures fro storing <TypeQ> with <Metalist>, referencies to text? Questiona and possible an-
swers. – Data structure Dictionary is selected.
Develop and test classes for serialization and deserialization of Dictionaries [6].
Develop and text classes for parsing text files.
Develop approaches of searching metainformation in text that corresponds to the type of question.
Prepare web-layer that visualizes results of searching.
The development of project is started from the class allowing to save and restore XML file from hard disk.
Storing and restoring is made by means of XML serialization. Class dataStore incapsulates the serialization and dese-
rialization operations.
class dataStore {
public string filename { get; set; }
public Dictionary<string,List<string>> quest_ans { get; set;}
public void SerializeD() { }
public void DeSerializeD() { }
}
The next class should support basic operations with datastore. (Something like CRUD operations when data-
bases are processed.) Class ManageDataStore implements datastore processing operations.
class ManageDataStore {
dataStore ds { get; set; }
public void DataStore_Create() { }
public void DataStore_Edit() { }
public void DataStore_View() { }
public void DataStore_Delete() { }
}
Class Language is aimed to proceed operation of searching answers in texts.
class Language {
dataStore ds { get; set; }
public string text { get; set; }
public List<string> questions { get; set; }
public List<string> answers { get; set; }
Language(string text, List<string> questions, string Lang) {
}
public void GetInformation() { }
public void FindAnswewrs() { }
}
Conclusion
Paper proposes the approach of searching information in texts. Searching procedure matches type of the
question and the specific metainformation in text. Experimental results show that the proposed approach has the
next advantages:
It allows reducing amount of text to be proceeded to find an answer to the question.
Approach works with the same effectiveness for different languages.
287
Програмні засоби штучного інтелекту
Key features of the approach:
It is quick and easy for realization of the software system.
It allows extending metainformation both about new types of questions and about specific metainformation
in the texts without modification of code.
It is realized for easily adding of Latinic and Cyrillic languages. In order to add new language it is not nec-
essary to modify code. Only new XML files with key features about questions and metainformation for answers in
texts must be added.
Drawback of the approach: as it is the first version of the approach it allows to find the same answer for
different questions of the same type (see case study). This paper starts the cycle ow works, representing results of
intelligent texts’ search.
The defined drawback is basic for representing the further research:
Development of the approach of questions’ normalization. This approach will search answers according
to questions’ type and other keywords in the text of the question. For example, questions “When did the eldest
son wait for the apple?” and “When the apple was riped?” receive the same answers (see case study). Normal-
ization approach will allow avoiding this drawback.
References
1. Furlan, B., Batanović, V., Nikolić, B. Semantic similarity of short texts in languages with a deficient natural language processing support.
Decision Support Systems, 2013. 55(3), 710-719.
2. Ngompé, G. T., Harispe, S., Zambrano, G., Montmain, J., & Mussard, S. Detecting sections and entities in court decisions using HMM and
CRF graphical models. In Advances in Knowledge Discovery and Management 2019.
3. Mahdi, Q. A., Zhyvotovskyi, R., Kravchenko, S., Borysov, I., Orlov, O., Panchenko, I., & Boholii, S. Development of a Method of Structur-
al-Parametric Assessment of the Object State. Eastern-European Journal of Enterprise Technologies, 2021. 5(4), 113.1-86. Springer, Cham.
4. Gizun, A., Hriha, V., Roshchuk, M., Yevchenko, Y., & Hu, Z. Method of informational and psychological influence evaluation in social
networks based on fuzzy logic. Paper presented at the CEUR Workshop Proceedings, 2019., 2392
5. Text for case study https://bulgarianhistory.org/trimata-bratq-i-zlatnata-qbalka/
6. Dictionary serialization https://stackoverflow.com/questions/14304034/serialize-dictionarystring-liststring-into-xml
Bibliography
1. Furlan, B., Batanović, V., Nikolić, B. Semantic similarity of short texts in languages with a deficient natural language processing support.
Decision Support Systems, 2013. 55(3), 710-719.
2. Ngompé, G. T., Harispe, S., Zambrano, G., Montmain, J., & Mussard, S. Detecting sections and entities in court decisions using HMM and
CRF graphical models. In Advances in Knowledge Discovery and Management 2019.
3. Mahdi, Q. A., Zhyvotovskyi, R., Kravchenko, S., Borysov, I., Orlov, O., Panchenko, I., & Boholii, S. Development of a Method of Structur-
al-Parametric Assessment of the Object State. Eastern-European Journal of Enterprise Technologies, 2021. 5(4), 113.1-86. Springer, Cham.
4. Gizun, A., Hriha, V., Roshchuk, M., Yevchenko, Y., & Hu, Z. Method of informational and psychological influence evaluation in social
networks based on fuzzy logic. Paper presented at the CEUR Workshop Proceedings, 2019., 2392
5. Текс для експерименту https://bulgarianhistory.org/trimata-bratq-i-zlatnata-qbalka/
6. Серіалізація словника https://stackoverflow.com/questions/14304034/serialize-dictionarystring-liststring-into-xml
Received 04.08.2022
About the authors:
Olena Chebanyuk
Doctor of Sciences, Assoc. professor
Glushkov Institute of Cybernetics, department 205,
senior researcher,
Approximately 25 Ukrainian publications,
Approximately 60 International publications,
H-index: Google Scholar – 6,
Scopus – 2,
0000-0002-9873-6010.
Place of work:
National Aviation University,
Software engineering Department,
Lubomir Guzar ave 1
Telegram @tocarelcielo
288
Програмні засоби штучного інтелекту
Glushkov Institute of Cybernetics of
National Academy of Sciences of Ukraine,
40 Glushkov ave., Kyiv, Ukraine, 03187,
tel.: (+38) (044) 526 3348
email: Chebanyuk.olena@gmail.com
Прізвище та ім’я автора і назва доповіді англійською мовою:
Chebanuyk O.V.
An approach of intelligent searching of information in texts
Прізвище та ім’я автора і назва доповіді українською мовою:
Чебанюк О. B.
Методика аналітичного пошуку інформації у текстах
Контакти для редактора: Чебанюк Олена Вікторівна,
старший науковий співробітник відділ 205 інститут кібернетики
ім В.М. Глушкова НАН України,
e-mail: Chebanyuk.olena@gmail.com
тел.: телеграм @tocarelcielo вайбер 091-619-54-24
|