Standard
Extraction of explicit consumer intentions from social network messages. / Pimenov, Ivan; Salomatina, Natalia.
Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. ред. / Alexander Panchenko; Wil M. van der Aalst; Michael Khachay; Panos M. Pardalos; Vladimir Batagelj; Natalia Loukachevitch; Goran Glavaš; Dmitry I. Ignatov; Sergei O. Kuznetsov; Olessia Koltsova; Irina A. Lomazova; Andrey V. Savchenko; Amedeo Napoli; Marcello Pelillo. Springer-Verlag GmbH and Co. KG, 2018. стр. 127-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11179 LNCS).
Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Harvard
Pimenov, I & Salomatina, N 2018,
Extraction of explicit consumer intentions from social network messages. в A Panchenko, WM van der Aalst, M Khachay, PM Pardalos, V Batagelj, N Loukachevitch, G Glavaš, DI Ignatov, SO Kuznetsov, O Koltsova, IA Lomazova, AV Savchenko, A Napoli & M Pelillo (ред.),
Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 11179 LNCS, Springer-Verlag GmbH and Co. KG, стр. 127-133, 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, Moscow, Российская Федерация,
05.07.2018.
https://doi.org/10.1007/978-3-030-11027-7_13APA
Pimenov, I., & Salomatina, N. (2018).
Extraction of explicit consumer intentions from social network messages. в A. Panchenko, W. M. van der Aalst, M. Khachay, P. M. Pardalos, V. Batagelj, N. Loukachevitch, G. Glavaš, D. I. Ignatov, S. O. Kuznetsov, O. Koltsova, I. A. Lomazova, A. V. Savchenko, A. Napoli, & M. Pelillo (Ред.),
Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers (стр. 127-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 11179 LNCS). Springer-Verlag GmbH and Co. KG.
https://doi.org/10.1007/978-3-030-11027-7_13Vancouver
Pimenov I, Salomatina N.
Extraction of explicit consumer intentions from social network messages. в Panchenko A, van der Aalst WM, Khachay M, Pardalos PM, Batagelj V, Loukachevitch N, Glavaš G, Ignatov DI, Kuznetsov SO, Koltsova O, Lomazova IA, Savchenko AV, Napoli A, Pelillo M, Редакторы, Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Springer-Verlag GmbH and Co. KG. 2018. стр. 127-133. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-11027-7_13
Author
Pimenov, Ivan ; Salomatina, Natalia. /
Extraction of explicit consumer intentions from social network messages. Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers. Редактор / Alexander Panchenko ; Wil M. van der Aalst ; Michael Khachay ; Panos M. Pardalos ; Vladimir Batagelj ; Natalia Loukachevitch ; Goran Glavaš ; Dmitry I. Ignatov ; Sergei O. Kuznetsov ; Olessia Koltsova ; Irina A. Lomazova ; Andrey V. Savchenko ; Amedeo Napoli ; Marcello Pelillo. Springer-Verlag GmbH and Co. KG, 2018. стр. 127-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{14a9443112c0455da73866be33eb346e,
title = "Extraction of explicit consumer intentions from social network messages",
abstract = "In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.",
keywords = "Directed graph, Fact extraction, Intention, Intention marker, Machine learning with annotation",
author = "Ivan Pimenov and Natalia Salomatina",
note = "Funding Information: Acknowledgements. The study was supported by the Russian Academy of Science (the Program of Basic Research, project 0314-2016-0015). Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018 ; Conference date: 05-07-2018 Through 07-07-2018",
year = "2018",
doi = "10.1007/978-3-030-11027-7_13",
language = "English",
isbn = "9783030110260",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "127--133",
editor = "Alexander Panchenko and {van der Aalst}, {Wil M.} and Michael Khachay and Pardalos, {Panos M.} and Vladimir Batagelj and Natalia Loukachevitch and Goran Glava{\v s} and Ignatov, {Dmitry I.} and Kuznetsov, {Sergei O.} and Olessia Koltsova and Lomazova, {Irina A.} and Savchenko, {Andrey V.} and Amedeo Napoli and Marcello Pelillo",
booktitle = "Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers",
address = "Germany",
}
RIS
TY - GEN
T1 - Extraction of explicit consumer intentions from social network messages
AU - Pimenov, Ivan
AU - Salomatina, Natalia
N1 - Funding Information:
Acknowledgements. The study was supported by the Russian Academy of Science (the Program of Basic Research, project 0314-2016-0015).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.
AB - In this paper we address the problem of automatic extraction of facts from Russian texts. The facts under examination are the intentions of social network users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows distinguishing between various intentional blocks: objects, their different properties and emphatic constructions. Pre-processing of the training set includes lemmatization and grammatical tagging with PyMorphy2. Then, on the material of the training set, a directed graph is constructed. Each node in this graph corresponds to an intentional block, including information about its expertly-assigned intentional tag, grammatical and/or lexical properties of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set includes both messages containing non-consumer intentions or no intentions at all. The precision and recall of intention extraction with macro average is 82% and 74% respectively.
KW - Directed graph
KW - Fact extraction
KW - Intention
KW - Intention marker
KW - Machine learning with annotation
UR - http://www.scopus.com/inward/record.url?scp=85059947568&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11027-7_13
DO - 10.1007/978-3-030-11027-7_13
M3 - Conference contribution
AN - SCOPUS:85059947568
SN - 9783030110260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 127
EP - 133
BT - Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers
A2 - Panchenko, Alexander
A2 - van der Aalst, Wil M.
A2 - Khachay, Michael
A2 - Pardalos, Panos M.
A2 - Batagelj, Vladimir
A2 - Loukachevitch, Natalia
A2 - Glavaš, Goran
A2 - Ignatov, Dmitry I.
A2 - Kuznetsov, Sergei O.
A2 - Koltsova, Olessia
A2 - Lomazova, Irina A.
A2 - Savchenko, Andrey V.
A2 - Napoli, Amedeo
A2 - Pelillo, Marcello
PB - Springer-Verlag GmbH and Co. KG
T2 - 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018
Y2 - 5 July 2018 through 7 July 2018
ER -
