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Publications (10 of 42) Show all publications
Kumar, U., Reganti, A. N., Maheshwari, T., Chakroborty, T., Gambäck, B. & Das, A. (2018). Inducing Personalities and Values from Language Use in Social Network Communities. Information Systems Frontiers, 20(6), 1219-1240
Open this publication in new window or tab >>Inducing Personalities and Values from Language Use in Social Network Communities
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2018 (English)In: Information Systems Frontiers, ISSN 1387-3326, E-ISSN 1572-9419, Vol. 20, no 6, p. 1219-1240Article in journal (Refereed) Published
Abstract [en]

A community in social networks is generally assumed to be composed of a group of individuals with similar characteristics. Although there has been a plethora of work on understanding network topologies (edge density, clustering coefficient, etc.) within an online community, the psycho-sociological compositions of social network communities have hardly been studied. The present paper aims to analyse the communities as composition of induced psycholinguistic and sociolinguistic variables (Personalities, Values and Ethics) across individuals in social media networks. The motivation behind this analysis is to understand the behavioural characteristics at individual as well as societal level in social networks. To this end, three studies were carried out on six different datasets: three Twitter corpora, two Facebook corpora, and an Essay corpus, annotated with Values and Ethics of the users. First, experiments on creating automatic models to determine the Personality and Values of individuals by analysing their language usage and social media behaviour. Second, experiments on understanding the characteristics or blend of characteristics of individuals within an online community. Finally, generation of a map of values and ethics for India, a multi-lingual and multi-cultural country. Striking similarities to general intuitive perception could be observed, i.e., the results obtained in the study resemble our general perception about the cities/towns of India. 

Place, publisher, year, edition, pages
Springer New York LLC, 2018
Keywords
Community, Ethics, Personality, Social network, Values, Linguistics, Online systems, Philosophical aspects, Clustering coefficient, Network communities, On-line communities, Social media networks, Social networking (online)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ri:diva-37955 (URN)10.1007/s10796-017-9793-8 (DOI)2-s2.0-85029010113 (Scopus ID)
Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-05-03Bibliographically approved
Maheshwari, T., Reganti, A. N., Gupta, S., Jamatia, A., Kumar, U., Gambäck, B. & Das, A. (2017). A societal sentiment analysis: Predicting the values and ethics of individuals by analysing social media content. In: 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference: . Paper presented at 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, 3 April 2017 through 7 April 2017 (pp. 731-741).
Open this publication in new window or tab >>A societal sentiment analysis: Predicting the values and ethics of individuals by analysing social media content
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2017 (English)In: 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 2017, p. 731-741Conference paper, Published paper (Refereed)
Abstract [en]

To find out how users' social media behaviour and language are related to their ethical practices, the paper investigates applying Schwartz' psycholinguistic model of societal sentiment to social media text. The analysis is based on corpora collected from user essays as well as social media (Facebook and Twitter). Several experiments were carried out on the corpora to classify the ethical values of users, incorporating Linguistic Inquiry Word Count analysis, n-grams, topic models, psycholinguistic lexica, speech-acts, and nonlinguistic information, while applying a range of machine learners (Support Vector Machines, Logistic Regression, and Random Forests) to identify the best linguistic and non-linguistic features for automatic classification of values and ethics.

Keywords
Classification (of information), Computational linguistics, Decision trees, Linguistics, Philosophical aspects, Automatic classification, Ethical practices, Ethical values, Linguistic features, Logistic regressions, Machine learners, Psycholinguistic models, Sentiment analysis, Social networking (online)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-31136 (URN)10.18653/v1/e17-1069 (DOI)2-s2.0-85021625321 (Scopus ID)9781510838604 (ISBN)
Conference
15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, 3 April 2017 through 7 April 2017
Available from: 2017-08-28 Created: 2017-08-28 Last updated: 2019-08-14Bibliographically approved
Gambäck, B., Olsson, F. & Täckström, O. (2011). Active Learning for Dialogue Act Classification (9ed.). In: : . Paper presented at INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association.
Open this publication in new window or tab >>Active Learning for Dialogue Act Classification
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Active learning techniques were employed for classification of dialogue acts over two dialogue corpora, the English human-human Switchboard corpus and the Spanish human-machine Dihana corpus. It is shown clearly that active learning improves on a baseline obtained through a passive learning approach to tagging the same data sets. An error reduction of 7% was obtained on Switchboard, while a factor 5 reduction in the amount of labeled data needed for classification was achieved on Dihana. The passive Support Vector Machine learner used as baseline in itself significantly improves the state of the art in dialogue act classification on both corpora. On Switchboard it gives a 31% error reduction compared to the previously best reported result.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23877 (URN)
Conference
INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association
Projects
COMPANIONS
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Wilks, Y., Gambäck, B. & Danieli, M. (Eds.). (2010). Workshop on Companionable Dialogue Systems (6ed.). Uppsala, Sweden: ACL
Open this publication in new window or tab >>Workshop on Companionable Dialogue Systems
2010 (English)Collection (editor) (Refereed)
Place, publisher, year, edition, pages
Uppsala, Sweden: ACL, 2010. p. 59 Edition: 6
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23896 (URN)978-1-932432-81-7 (ISBN)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Ståhl, O., Gambäck, B., Turunen, M. & Hakulinen, J. (2009). A Mobile Health and Fitness Companion Demonstrator (11ed.). In: : . Paper presented at 12th Conference of the European Chapter of the Association for Computational Linguistics, ACL (pp. 65-68).
Open this publication in new window or tab >>A Mobile Health and Fitness Companion Demonstrator
2009 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. The paper presents a multimodal conversational Companion system focused on health and fitness, which has both a stationary and a mobile component.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23676 (URN)
Conference
12th Conference of the European Chapter of the Association for Computational Linguistics, ACL
Projects
COMPANIONS
Note

Published in Proceedings of the Demonstrations Session at EACL 2009

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Gambäck, B., Olsson, F., Argaw, A. A. & Asker, L. (2009). Methods for Amharic part-of-speech tagging (1ed.). In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: . Paper presented at First Workshop on Language Technologies for African Languages, March 2009, Athens, Greece.
Open this publication in new window or tab >>Methods for Amharic part-of-speech tagging
2009 (English)In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, 2009, 1, , p. 8Conference paper, Published paper (Refereed)
Abstract [en]

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results.

Publisher
p. 8
Keywords
part-of-speech tagging, amharic, machine learning
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23519 (URN)
Conference
First Workshop on Language Technologies for African Languages, March 2009, Athens, Greece
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Ståhl, O., Gambäck, B., Hansen, P., Turunen, M. & Hakulinen, J. (2008). A mobile fitness companion (2ed.). In: : . Paper presented at The Fourth International Workshop on Human-Computer Conversation.
Open this publication in new window or tab >>A mobile fitness companion
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2008 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The paper introduces a Mobile Companion prototype, which helps users to plan and keep track of their exercise activities via an interface based mainly on speech input and output. The Mobile Companion runs on a PDA and is based on a stand-alone, speaker-independent solution, making it fairly unique among mobile spoken dialogue systems, where the common solution is to run the ASR on a separate server or to restrict the speech input to some specific set of users. The prototype uses a GPS receiver to collect position, distance and speed data while the user is exercising, and allows the data to be compared to previous exercises. It communicates over the mobile network with a stationary system, placed in the user’s home. This allows plans for exercise activities to be downloaded from the stationary to the mobile system, and exercise result data to be uploaded once an exercise has been completed.

Publisher
p. 6
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23465 (URN)
Conference
The Fourth International Workshop on Human-Computer Conversation
Projects
COMPANIONS
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Turunen, M., Hakulinen, J., Ståhl, O., Gambäck, B., Hansen, P., Rodríguez Gancedo, M. C., . . . Cavazza, M. (2008). Multimodal agent interfaces and system architectures for health and fitness companions (2ed.). In: : . Paper presented at The Fourth International Workshop on Human-Computer Conversation.
Open this publication in new window or tab >>Multimodal agent interfaces and system architectures for health and fitness companions
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2008 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Multimodal conversational spoken dialogues using physical and virtual agents provide a potential interface to motivate and support users in the domain of health and fitness. In this paper we present how such multimodal conversational Companions can be implemented to support their owners in various pervasive and mobile settings. In particular, we focus on different forms of multimodality and system architectures for such interfaces.

Publisher
p. 6
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-23466 (URN)
Conference
The Fourth International Workshop on Human-Computer Conversation
Projects
COMPANIONS
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Gambäck, B., Sahlgren, M., Argaw, A. A. & Asker, L. (2006). Applying machine learning to Amharic text classification (1ed.). In: : . Paper presented at WOCAL 5: 5th World Congress of African Linguistics, 7-11 August 2006, Addis Ababa University, Ethiopia.
Open this publication in new window or tab >>Applying machine learning to Amharic text classification
2006 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-21169 (URN)
Conference
WOCAL 5: 5th World Congress of African Linguistics, 7-11 August 2006, Addis Ababa University, Ethiopia
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-21Bibliographically approved
Seid, H. & Gambäck, B. (2005). A Speaker Independent Continuous Speech Recognizer for Amharic (1ed.). In: : . Paper presented at INTERSPEECH 2005, 9th European Conference on Speech Communication and Technology.
Open this publication in new window or tab >>A Speaker Independent Continuous Speech Recognizer for Amharic
2005 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The paper discusses an Amharic speaker independent continuous speech recognizer based on an HMM/ANN hybrid approach. The model was constructed at a context dependent phone part sub-word level with the help of the CSLU Toolkit. A promising result of 74.28% word and 39.70% sentence recognition rate was achieved. These are the best figures reported so far for speech recognition for the Amharic language.

Publisher
p. 4
Keywords
Automatic Speech Recognition, Hidden Markov Models, Artificial Neural Networks, Amharic
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-21055 (URN)
Conference
INTERSPEECH 2005, 9th European Conference on Speech Communication and Technology
Note

pp. 3349-3352.

Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-16Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5252-707x

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