Timo Honkela


Co-Operation as an Asymmetric Form of Human-Computer Creativity. Case: Peace Machine
Mika Hämäläinen | Timo Honkela
Proceedings of the First Workshop on NLP for Conversational AI

This theoretical paper identifies a need for a definition of asymmetric co-creativity where creativity is expected from the computational agent but not from the human user. Our co-operative creativity framework takes into account that the computational agent has a message to convey in a co-operative fashion, which introduces a trade-off on how creative the computer can be. The requirements of co-operation are identified from an interdisciplinary point of view. We divide co-operative creativity in message creativity, contextual creativity and communicative creativity. Finally these notions are applied in the context of the Peace Machine system concept.


Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation
Emily Öhman | Kaisla Kajava | Jörg Tiedemann | Timo Honkela
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, Sentimentator, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.


The Challenges of Multi-dimensional Sentiment Analysis Across Languages
Emily Öhman | Timo Honkela | Jörg Tiedemann
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

This paper outlines a pilot study on multi-dimensional and multilingual sentiment analysis of social media content. We use parallel corpora of movie subtitles as a proxy for colloquial language in social media channels and a multilingual emotion lexicon for fine-grained sentiment analyses. Parallel data sets make it possible to study the preservation of sentiments and emotions in translation and our assessment reveals that the lexical approach shows great inter-language agreement. However, our manual evaluation also suggests that the use of purely lexical methods is limited and further studies are necessary to pinpoint the cross-lingual differences and to develop better sentiment classifiers.


Likey: Unsupervised Language-Independent Keyphrase Extraction
Mari-Sanna Paukkeri | Timo Honkela
Proceedings of the 5th International Workshop on Semantic Evaluation


Speech to speech machine translation: Biblical chatter from Finnish to English
David Ellis | Mathias Creutz | Timo Honkela | Mikko Kurimo
Proceedings of the IJCNLP-08 Workshop on NLP for Less Privileged Languages

A Language-Independent Approach to Keyphrase Extraction and Evaluation
Mari-Sanna Paukkeri | Ilari T. Nieminen | Matti Pöllä | Timo Honkela
Coling 2008: Companion volume: Posters


AWAREDAG-transformations for Semantic Analysis
Aarno Lehtola | Timo Honkela
Proceedings of the 6th Nordic Conference of Computational Linguistics (NODALIDA 1987)

Predication Graphs as Canonical Representation of Query Sentences
Timo Honkela | Aarno Lehtola | K. Valkonen
Proceedings of the 6th Nordic Conference of Computational Linguistics (NODALIDA 1987)