Detecting Early Signs of Depression in the Conversational Domain: The Role of Transfer Learning in Low-Resource Scenarios

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359317" target="_blank" >RIV/68407700:21230/22:00359317 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/22:00359317

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-08473-7_33" target="_blank" >https://doi.org/10.1007/978-3-031-08473-7_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08473-7_33" target="_blank" >10.1007/978-3-031-08473-7_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detecting Early Signs of Depression in the Conversational Domain: The Role of Transfer Learning in Low-Resource Scenarios

Popis výsledku v původním jazyce

The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on detecting depression in the domain of social media, where there is a sufficient amount of data. However, with the rise of conversational agents like Siri or Alexa, the conversational domain is becoming more critical. Unfortunately, there is a lack of data in the conversational domain. We perform a study focusing on domain adaptation from social media to the conversational domain. Our approach mainly exploits the linguistic information preserved in the vector representation of text. We describe transfer learning techniques to classify users who suffer from early signs of depression with high recall. We achieve state-of-the-art results on a commonly used conversational dataset, and we highlight how the method can easily be used in conversational agents. We publicly release all source code.

Název v anglickém jazyce

Detecting Early Signs of Depression in the Conversational Domain: The Role of Transfer Learning in Low-Resource Scenarios

Popis výsledku anglicky

The high prevalence of depression in society has given rise to the need for new digital tools to assist in its early detection. To this end, existing research has mainly focused on detecting depression in the domain of social media, where there is a sufficient amount of data. However, with the rise of conversational agents like Siri or Alexa, the conversational domain is becoming more critical. Unfortunately, there is a lack of data in the conversational domain. We perform a study focusing on domain adaptation from social media to the conversational domain. Our approach mainly exploits the linguistic information preserved in the vector representation of text. We describe transfer learning techniques to classify users who suffer from early signs of depression with high recall. We achieve state-of-the-art results on a commonly used conversational dataset, and we highlight how the method can easily be used in conversational agents. We publicly release all source code.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Natural Language Processing and Information Systems

ISBN

978-3-031-08472-0

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

358-369

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Valencia

Datum konání akce

15. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000870296500033