Lena Hettinger, Dipl.-Math. - Data Science Chair

Science Manager

Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany

Email: lena.hettinger[at]uni-wuerzburg.de

Phone: (+49 931) 31 - 89675

Office: Room 50.03.003 (Zentrum für Künstliche Intelligenz und Data Science (CAIDAS))

Office Hours: Mon - Thu 9.00 - 11.00

About me

Natural Language Processing - Machine Learning - Digital Humanities

I have been part of the DMIR Research Group since I received my diploma degree in Mathematics in 2012 at the University of Würzburg. For the first two years I was mainly teaching, afterwards joining the Kallimachos group to research genre distributions in German novels. In 2016/17 I was on maternal leave, followed by work on transfer learning in the context of NLP to improve supervised tasks like semantic relation classification. I am interested in processing textual data to extend digital and human knowledge about language.

Since 2019 I am responsible for the administrative and scientific management of the newly established Data Science Chair. Since 2021 I am acting as deputy head of the Servicezentrum Informatik/CAIDAS (SIC).

Activities

Local Chair ECMLPKDD 2019
Women's Representative for the Institute of Computer Science at the University of Würzburg (2013-2015)

Publications

2020[ to top ]

Omeliyanenko, J., Zehe, A., Hettinger, L., and Hotho, A. (2020) “LM4KG: Improving Common Sense Knowledge Graphs with Language Models”, in International Semantic Web Conference, Springer.
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@inproceedings{omeliyanenko2020reweight, author = {Omeliyanenko, Janna and Zehe, Albin and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Semantic Web Conference}, keywords = {graph}, organization = {Springer}, title = {LM4KG: Improving Common Sense Knowledge Graphs with Language Models}, year = 2020 }
Tritscher, J., Ring, M., Schlör, D., Hettinger, L., and Hotho, A. (2020) “Evaluation of post-hoc XAI approaches through synthetic tabular data”, International Symposium on Methodologies for Intelligent Systems.
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Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.

@article{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, author = {Tritscher, Julian and Ring, Markus and Schlör, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Symposium on Methodologies for Intelligent Systems}, keywords = {XAI}, note = {Accepted but not published}, organization = {Springer}, title = {Evaluation of post-hoc XAI approaches through synthetic tabular data}, year = 2020 }
Zehe, A., Arns, J., Hettinger, L., and Hotho, A. (2020) “HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis”, in 5th SwissText & 16th KONVENS Joint Conference.
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@inproceedings{zehe2020harrymotions, author = {Zehe, Albin and Arns, Julia and Hettinger, Lena and Hotho, Andreas}, booktitle = {5th SwissText & 16th KONVENS Joint Conference}, keywords = {bert}, title = {HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis}, year = 2020 }

2019[ to top ]

Hettinger, L., Zehe, A., Dallmann, A., and Hotho, A. (2019) “EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification”, in David, K., Geihs, K., Lange, M. and Stumme, G., eds., INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Bonn: Gesellschaft für Informatik e.V., 191–204.
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@inproceedings{mci/Hettinger2019, address = {Bonn}, author = {Hettinger, Lena and Zehe, Albin and Dallmann, Alexander and Hotho, Andreas}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, editor = {David, Klaus and Geihs, Kurt and Lange, Martin and Stumme, Gerd}, keywords = {classification}, pages = {191-204}, publisher = {Gesellschaft für Informatik e.V.}, title = {EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification}, year = 2019 }
Zehe, A., Hettinger, L., Ernst, S., Hauptmann, C., and Hotho, A. (2019) “Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks”, in Proceedings of The 13th International Workshop on Semantic Evaluation, Association for Computational Linguistics.
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@inproceedings{zehe2019xenophilius, author = {Zehe, Albin and Hettinger, Lena and Ernst, Stefan and Hauptmann, Christian and Hotho, Andreas}, booktitle = {Proceedings of The 13th International Workshop on Semantic Evaluation}, keywords = {nn}, publisher = {Association for Computational Linguistics}, title = {Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks}, year = 2019 }
Koopmann, T., Dallmann, A., Hettinger, L., Niebler, T., and Hotho, A. (2019) “On the Right Track! Analysing and Predicting Navigation Success in Wikipedia”, in Proceedings of the 30th ACM Conference on Hypertext and Social Media, HT ’19, Hof, Germany: ACM, 143–152, available: https://doi.org/10.1145/3342220.3343650.
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@inproceedings{Koopmann:2019:RTA:3342220.3343650, address = {New York, NY, USA}, author = {Koopmann, Tobias and Dallmann, Alexander and Hettinger, Lena and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of the 30th ACM Conference on Hypertext and Social Media}, keywords = {user}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }

2018[ to top ]

Hettinger, L., Dallmann, A., Zehe, A., Niebler, T., and Hotho, A. (2018) “ClaiRE at SemEval-2018 Task 7 - Extended Version”, available: http://arxiv.org/abs/1804.05825.
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In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.

@misc{hettinger2018claire, abstract = {In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, keywords = {nn}, note = {cite arxiv:1804.05825Comment: This is the extended version for our work: ClaiRE at SemEval-2018 Task 7}, title = {ClaiRE at SemEval-2018 Task 7 - Extended Version}, year = 2018 }
Hettinger, L., Dallmann, A., Zehe, A., Niebler, T., and Hotho, A. (2018) “ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings”, in Proceedings of International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, USA.
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@inproceedings{hettinger2018semeval, address = {New Orleans, LA, USA}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)}, keywords = {word}, title = {ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings}, year = 2018 }

2016[ to top ]

Zehe, A., Becker, M., Hettinger, L., Hotho, A., Reger, I., and Jannidis, F. (2016) “Prediction of Happy Endings in German Novels”, in Cellier, P., Charnois, T., Hotho, A., Matwin, S., Moens, M.-F. and Toussaint, Y., eds., Proceedings of the Workshop on Interactions Between Data Mining and Natural Language Processing 2016, 9–16, available: http://ceur-ws.org/Vol-1646/.
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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 = {novels}, month = {07}, pages = {9-16}, title = {Prediction of Happy Endings in German Novels}, year = 2016 }
Hettinger, L., Jannidis, F., Reger, I., and Hotho, A. (2016) “Classification of Literary Subgenres”, in DHd 2016.
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@inproceedings{hettinger2016classification, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DHd 2016}, keywords = {classification}, title = {Classification of Literary Subgenres}, year = 2016 }
Jannidis, F., Reger, I., Zehe, A., Becker, M., Hettinger, L., and Hotho, A. (2016) “Analyzing Features for the Detection of Happy Endings in German Novels”, available: http://arxiv.org/abs/1611.09028.
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With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.

@misc{jannidis2016analyzing, abstract = {With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.}, author = {Jannidis, Fotis and Reger, Isabella and Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas}, keywords = {novels}, note = {cite arxiv:1611.09028}, title = {Analyzing Features for the Detection of Happy Endings in German Novels}, year = 2016 }
Hettinger, L., Jannidis, F., Reger, I., and Hotho, A. (2016) “Significance Testing for the Classification of Literary Subgenres”, in DH 2016.
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@inproceedings{hettinger2016significance, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DH 2016}, keywords = {classification}, title = {Significance Testing for the Classification of Literary Subgenres}, year = 2016 }

2015[ to top ]

Hettinger, L., Becker, M., Reger, I., Jannidis, F., and Hotho, A. (2015) “Genre classification on German novels”, in Proceedings of the 12th International Workshop on Text-Based Information Retrieval, available: http://www.uni-weimar.de/medien/webis/events/tir-15/tir15-papers-final/Hettinger2015-tir-paper.pdf.
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@inproceedings{hettinger2015genre, author = {Hettinger, Lena and Becker, Martin and Reger, Isabella and Jannidis, Fotis and Hotho, Andreas}, booktitle = {Proceedings of the 12th International Workshop on Text-based Information Retrieval}, keywords = {classification}, title = {Genre classification on German novels}, year = 2015 }

Teaching

WS 2018/19: Ausgewählte Themen des Machine Learning (seminar)

SS 2018: Ausgewählte Themen des Machine Learning (seminar)

WS 2015/16: Ausgewählte Themen des Web 2.0 (seminar)
WS 2015/16: Algorithmen und Datenstrukturen für WiInf und MCS (tutorials)

SS 2015: Ausgewählte Themen des Web 2.0 (seminar)
WS 2014/15: Algorithmen und Datenstrukturen für WiInf und MCS (tutorials)

SS 2014: Ausgewählte Themen des Web 2.0 (seminar)
WS 2013/14: Ausgewählte Themen des Web 2.0 (seminar)
WS 2013/14: Algorithmen und Datenstrukturen für WiInf und MCS (tutorials)

SS 2013: Ausgewählte Themen des Web 2.0 (seminar)
WS 2012/13: Ausgewählte Themen des Web 2.0 (seminar)
WS 2012/13: Algorithmen und Datenstrukturen für WiInf und MCS (tutorials)

Natural Language Processing - Machine Learning - Digital Humanities

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