Albin Zehe, M.Sc.

Albin Zehe

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany

Email: zehe <at> informatik.uni-wuerzburg.de

Phone: (+49 931) 31 - 83217

Office: Room B114 (Computer Science Building M2)

PGP-Key

Fingerprint: 4595 A504 61B7 CF2C 43FA 0479 D761 1688 5D96 19FE

Research Interests

My main interests are in the areas of Text Mining, Neural Networks and Classification in general. I also like to push reluctant people towards doing research in NLP :)
My Master's Thesis was on Sentiment Analysis in the domain of literature, which is generally still my core research area: I'm trying to find new ways of analysing novels using computers.

In my spare time, I like to read books (mostly everything written by Tolkien ;)¹), watch TV shows and play Dota 2.

Projects

Teaching

Machine Learning for Natural Language Processing (Summer 2018, 2019, 2020, 2021, 2022, 2023)
Praktikum: Natural Language Processing (frequently)
Introduction to Algorithms and Data Structures (Winter Term 2017/2018)
Information Retrieval (Summer 2017)
Seminar Web 2.0 (Summer 2017)

Selected Publications

This is a list of selected publications I have co-authored.

A complete list can be found here.

2022[ to top ]

One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien’s Legendarium Perri, Vincenzo; Qarkaxhija, Lisi; Zehe, Albin; Hotho, Andreas; Scholtes, Ingo in Proceedings of the Computational Humanities Research Conference 2022, {CHR} 2022, Antwerp, Belgium, December 12-14, 2022, {CEUR} Workshop Proceedings, F. Karsdorp, K. L. Nielbo (eds.) (2022). (Vol. 3290) 291–317.
- [ BibTeX ]
- [ URL ]
- [ Download ]
@inproceedings{perri2022graph, author = {Perri, Vincenzo and Qarkaxhija, Lisi and Zehe, Albin and Hotho, Andreas and Scholtes, Ingo}, booktitle = {Proceedings of the Computational Humanities Research Conference 2022, {CHR} 2022, Antwerp, Belgium, December 12-14, 2022}, editor = {Karsdorp, Folgert and Nielbo, Kristoffer L.}, keywords = {tolkien}, pages = {291–317}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien's Legendarium}, volume = 3290, year = 2022 }

2021[ to top ]

Detecting Scenes in Fiction: A new Segmentation Task Zehe, Albin; Konle, Leonard; Dümpelmann, Lea; Gius, Evelyn; Hotho, Andreas; Jannidis, Fotis; Kaufmann, Lucas; Krug, Markus; Puppe, Frank; Reiter, Nils; Schreiber, Annekea; Wiedmer, Nathalie in Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (2021).
- [ BibTeX ]
- [ Download ]
@inproceedings{zehe2021detecting, author = {Zehe, Albin and Konle, Leonard and Dümpelmann, Lea and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea and Wiedmer, Nathalie}, booktitle = {Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, keywords = {task}, publisher = {ACL}, title = {Detecting Scenes in Fiction: A new Segmentation Task}, year = 2021 }

2020[ to top ]

HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis Zehe, Albin; Arns, Julia; Hettinger, Lena; Hotho, Andreas in 5th SwissText & 16th KONVENS Joint Conference (2020).
- [ BibTeX ]
- [ Download ]
@inproceedings{zehe2020harrymotions, author = {Zehe, Albin and Arns, Julia and Hettinger, Lena and Hotho, Andreas}, booktitle = {5th SwissText & 16th KONVENS Joint Conference}, keywords = {sentimentanalysis}, title = {HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis}, year = 2020 }
Time Series Forecasting for Self-Aware Systems {Bauer}, A.; {Züfle}, M.; {Herbst}, N.; {Zehe}, A.; {Hotho}, A.; {Kounev}, S. in Proceedings of the IEEE (2020). 1–26.
- [ Abstract ]
- [ BibTeX ]
Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.

@article{9069193, abstract = {Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.}, author = {{Bauer}, A. and {Züfle}, M. and {Herbst}, N. and {Zehe}, A. and {Hotho}, A. and {Kounev}, S.}, journal = {Proceedings of the IEEE}, keywords = {timeseries}, pages = {1-26}, title = {Time Series Forecasting for Self-Aware Systems}, year = 2020 }
Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels Kobs, Konstantin; Zehe, Albin; Bernstetter, Armin; Chibane, Julian; Pfister, Jan; Tritscher, Julian; Hotho, Andreas in ACM Transactions on Social Computing (2020).
- [ BibTeX ]
@article{noauthororeditor, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {ACM Transactions on Social Computing}, keywords = {twitch}, note = {accepted but not published}, title = {Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels}, year = 2020 }

2018[ to top ]

Analysing Direct Speech in German Novels Jannidis, Fotis; Konle, Leonard; Zehe, Albin; Hotho, Andreas; Krug, Markus in DHd 2018 (2018).
- [ BibTeX ]
- [ Download ]
@inproceedings{jannidis2018analysing, author = {Jannidis, Fotis and Konle, Leonard and Zehe, Albin and Hotho, Andreas and Krug, Markus}, booktitle = {DHd 2018}, keywords = {myown:selected}, title = {Analysing Direct Speech in German Novels}, year = 2018 }

2017[ to top ]

Towards Sentiment Analysis on German Literature Zehe, Albin; Becker, Martin; Jannidis, Fotis; Hotho, Andreas in Joint German/Austrian Conference on Artificial Intelligence (K{\"u}nstliche Intelligenz) (2017). 387–394.
- [ BibTeX ]
- [ Download ]
@inproceedings{zehe2017sentiment, author = {Zehe, Albin and Becker, Martin and Jannidis, Fotis and Hotho, Andreas}, booktitle = {Joint German/Austrian Conference on Artificial Intelligence (K{\"u}nstliche Intelligenz)}, keywords = {sentimentanalysis}, organization = {Springer}, pages = {387-394}, title = {Towards Sentiment Analysis on German Literature}, year = 2017 }

2016[ to top ]

Prediction of Happy Endings in German Novels Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas; Reger, Isabella; Jannidis, Fotis in Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016, P. Cellier, T. Charnois, A. Hotho, S. Matwin, M.-F. Moens, Y. Toussaint (eds.) (2016). 9–16.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ Download ]
Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.

@inproceedings{zehe2016prediction, abstract = {Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVM- classifier, which yields an average F1-score of about 73%.}, author = {Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis}, booktitle = {Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing 2016}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, keywords = {sentimentanalysis}, month = {jul}, pages = {9-16}, title = {Prediction of Happy Endings in German Novels}, year = 2016 }

Other Scientific Activities

Reviewing

Reviewer for ARR
PC member for ACL 2021 & 2023
PC member for EMNLP 2021 & 2022
PC member for ECMLPKDD 2020 & 2021
PC member for ECAI 2020
PC member for AAAI 2020, 2021 & 2022 Student Programs
PC member for LaTeCH-CLfL 2021, 2022, 2023
Reviewer for ZfdG
Reviewer for DHd 2019, 2020 & 2023
Subreviewer for several conferences and journals

Challenges and Shared Tasks

Co-Organiser of the ChAT challenge at ECML-PKDD 2020
Co-Organiser of the Shared Task on Scene Segmentation at KONVENS 2021

¹ This is a serious typographical issue.