КФУ. Карточка публикации. Exploring convolutional neural networks and topic models for user profiling from drug reviews

КАЗАНСКИЙ
ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ

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EXPLORING CONVOLUTIONAL NEURAL NETWORKS AND TOPIC MODELS FOR USER PROFILING FROM DRUG REVIEWS

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2017

Язык

русский

Авторы, сотрудники КФУ

Николенко Сергей Игоревич, автор

Тутубалина Елена Викторовна, автор

Библиографическое описание на языке оригинала

Tutubalina Elena, Nikolenko Sergey. Exploring convolutional neural networks and topic models for user profiling from drug reviews // Multimedia Tools and Applications. — 2017.

Аннотация

Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information represents a novel and exciting approach to personalized medicine and wide-scale drug tests. In medical applications, demographic information regarding the authors of these reviews such as age and gender is of primary importance; however, existing studies usually either assume that this information is available or overlook the issue entirely. In this work, we propose and compare several approaches to automated mining of demographic information from user-generated texts. We compare modern natural language processing techniques, including extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem.

Ключевые слова

text mining, natural language processing, topic modeling, deep learning, convolutional neural networks, multi-task learning, single-task learning, user reviews, demographic prediction, demographic attributes, social media, mental health

Название журнала

Multimedia Tools and Applications

URL

http://rdcu.be/yexM

Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку

https://repository.kpfu.ru/?p_id=167066

Файлы ресурса

Название файла	Размер (Мб)	Формат
exploring_convolutional_neural_4.pdf	0,91	pdf	посмотреть / скачать

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Николенко Сергей Игоревич	ru_RU
dc.contributor.author	Тутубалина Елена Викторовна	ru_RU
dc.date.accessioned	2017-01-01T00:00:00Z	ru_RU
dc.date.available	2017-01-01T00:00:00Z	ru_RU
dc.date.issued	2017	ru_RU
dc.identifier.citation	Tutubalina Elena, Nikolenko Sergey. Exploring convolutional neural networks and topic models for user profiling from drug reviews // Multimedia Tools and Applications. — 2017.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=167066	ru_RU
dc.description.abstract	Multimedia Tools and Applications	ru_RU
dc.description.abstract	Pharmacovigilance, and generally applications of natural language processing models to healthcare, have attracted growing attention over the recent years. In particular, drug reactions can be extracted from user reviews posted on the Web, and automated processing of this information represents a novel and exciting approach to personalized medicine and wide-scale drug tests. In medical applications, demographic information regarding the authors of these reviews such as age and gender is of primary importance; however, existing studies usually either assume that this information is available or overlook the issue entirely. In this work, we propose and compare several approaches to automated mining of demographic information from user-generated texts. We compare modern natural language processing techniques, including extensions of topic models and convolutional neural networks (CNN). We apply single-task and multi-task learning approaches to this problem.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	text mining	ru_RU
dc.subject	natural language processing	ru_RU
dc.subject	topic modeling	ru_RU
dc.subject	deep learning	ru_RU
dc.subject	convolutional neural networks	ru_RU
dc.subject	multi-task learning	ru_RU
dc.subject	single-task learning	ru_RU
dc.subject	user reviews	ru_RU
dc.subject	demographic prediction	ru_RU
dc.subject	demographic attributes	ru_RU
dc.subject	social media	ru_RU
dc.subject	mental health	ru_RU
dc.title	Exploring convolutional neural networks and topic models for user profiling from drug reviews	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU