Samuel Rönnqvist, PhD
Language technology & deep learning researcher

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I am a postdoc researcher @ TurkuNLP, University of Turku, Finland.

- Visiting researcher @ Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany
- PhD candidate @ Turku Centre for Computer Science / Data Mining Lab, Åbo Akademi Univ., Finland
- Associated researcher @ RiskLab, Arcada UAS, Finland
- CTO & Co-founder @ infolytika, Finland

Contact:  at


Find my research code on GitHub.


I am working on natural language generation/controllable text generation, explainable AI/interpretable NLP, machine learning visualization and application of NLP and text analytics in new domains.


Sentiment in Citizen Feedback: Exploration by Supervised Learning
Robin Lybeck, Samuel Rönnqvist and Sampo Ruoppila. In EGOV-CeDEM-ePart 2018 Proceedings (Electronic Government, E-Democracy/Open Government, and Electronic Participation Conference), ongoing research.

Deep Learning for Assessing Banks’ Distress from News and Numerical Financial Data
Paola Cerchiello, Giancarlo Nicola, Samuel Rönnqvist and Peter Sarlin. In Michael J. Brennan Irish Finance Working Paper Series, Research Paper No. 18-15.


Knowledge-Lean Text Mining
Samuel Rönnqvist. Doctoral thesis in Computer Science, Åbo Akademi University (TUCS Dissertations No 227).
  [Defense slides]

A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
Samuel Rönnqvist, Niko Schenk and Christian Chiarcos. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL).  
  [Source code] [Poster]

Bank distress in the news: Describing events through deep learning
Samuel Rönnqvist and Peter Sarlin. In Neurocomputing, Volume 264 (SI on Machine learning in finance).  
* Cited in Bloomberg View, "The Financial Threats That Machines Can See"
  [Neucomp version]


Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling
Niko Schenk, Christian Chiarcos, Kathrin Donandt, Samuel Rönnqvist, Evgeny A. Stepanov and Giuseppe Riccardi. In Proceedings of the Twentieth Conference on Computational Natural Language Learning - Shared Task, CoNLL 2016. Association for Computational Linguistics.  
  [Source code] [ACL Anthology]


Detect & Describe: Deep learning of bank stress in the news
Samuel Rönnqvist and Peter Sarlin. In Proceedings of the 2015 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).  
* Cited in The Riksbank Economic Commentary on Big Data

Bank networks from text: interrelations, centrality and determinants
Samuel Rönnqvist and Peter Sarlin. In Quantitative Finance, 15(10) (SI on Financial Data Analytics).
  [Live demo]   [QF version]   [ECB WP]   [Arcada WP]  

Exploratory topic modeling with distributional semantics
Samuel Rönnqvist. In Advances in Intelligent Data Analysis XIV, 241-252, Lecture Notes in Computer Science.    
  [Live demo]   [Source code]

Identifying bank stress by deep learning of news
Samuel Rönnqvist and Peter Sarlin. In Machine Learning Reports: Workshop New Challenges in Neural Computation 2015, 03/2015.  

Identifying financial risk from news: A semantic deep learning approach
Samuel Rönnqvist and Peter Sarlin. Presentation at Finnish Economic Association XXXVII Annual Meeting (KT-päivat), Helsinki, Finland.  


Combining human and computational intelligence through interactive visualization
Samuel Rönnqvist. Essay.  

Interactive Visual Exploration of Topic Models using Graphs
Samuel Rönnqvist, Xiaolu Wang and Peter Sarlin. In Proceedings of the Eurographics Conference on Visualization (EuroVis).    
  [Live demo]   [Source code]

Alluvial SOTM: Visualizing transitions and changes in cluster structure of the Self-Organizing Time Map
Samuel Rönnqvist and Peter Sarlin. In Proceedings of the Eurographics Conference on Visualization (EuroVis).    
  [Live demo]

From Text to Bank Interrelation Maps*
Samuel Rönnqvist and Peter Sarlin. In Proceedings of the 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).
  * Received IEEE CIFEr 1st Best Student Paper Award
  * Cited in Bank of England CCBS Handbook "Text mining for central banks"  


Green vs. non-green customer behavior: A Self-Organizing Time Map over greenness
Annika H. Holmbom, Samuel Rönnqvist, Peter Sarlin, Tomas Eklund and Barbro Back. In Proceedings of the 13th IEEE International Conference on Data Mining Workshops (ICDMW).  

Syntax-based modeling of topic relations
Samuel Rönnqvist. Presentation at Machine Learning Summer School, Tübingen, Germany.  

Cluster coloring of the Self-Organizing Map: An information visualization perspective
Peter Sarlin and Samuel Rönnqvist. In Proceedings of the 17th International Conference on Information Visualisation.    


Mapping Bank Interrelations in Financial Discussion
Samuel Rönnqvist et al. Presentation at The Eleventh International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland.    

Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations
Sofie Van Landeghem, Kai Hakala, Samuel Rönnqvist, Tapio Salakoski, Yves Van de Peer, and Filip Ginter. In Advances in Bioinformatics, Volume 2012.  
  [Online portal]


Visuality and Visualization of Information (spring 2015-2019, ÅAU): visual analytics (machine learning+interactive visualization), practical data visualization, information visualization theory.

Visual Analytics (fall 2016-2017, Arcada UAS): visualization for big data analytics, vis theory and practice, interactive visualization with d3.js.

Colloquium Applied Computational Linguistics (fall 2015, Uni. Frankfurt): tutorial on deep learning for NLP (word vectors and neural networks in Python).

Knowledge Management (fall 2015, Technische Hochschule Mittelhessen): tutorial on distributional semantics.

Data Mining and Text Mining (fall 2012–2015, ÅAU): text mining and practicals on statistical programming, analytics, text mining and machine learning.

Self-Organzing Maps Visualization Text Mining