Thesis/Theses examined by Schütze, Hinrich

Severini, Silvia (2023): Character-level and syntax-level models for low-resource and multilingual natural language processing. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Jalili Sabet, Masoud (2022): Multilingual representations and models for improved low-resource language processing. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Zhao, Mengjie (2022): Efficient transfer learning with pretrained language models. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Schick, Timo (2022): Few-shot learning with language models: Learning from instructions and contexts. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Schmitt, Martin (2022): Computational models of relations in text and knowledge graphs for logical reasoning and graph-text conversion. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Dufter, Philipp (2021): Distributed representations for multilingual language processing. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Sedinkina, Marina (2021): Domain adaptation in Natural Language Processing. Dissertation, LMU München: Faculty for Languages and Literatures

Pörner, Nina Mareike (2021): Combining contextualized and non-contextualized embeddings for domain adaptation and beyond. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Moiseeva, Alena (2020): Statistical natural language processing methods for intelligent process automation. Dissertation, LMU München: Faculty for Languages and Literatures

Gupta, Pankaj (2019): Neural information extraction from natural language text. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Kann, Katharina (2019): Neural sequence-to-sequence models for low-resource morphology. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Adel, Heike (2018): Deep learning methods for knowledge base population. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Yin, Wenpeng (2017): Deep neural networks for identification of sentential relations. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Yaghoobzadeh, Yadollah (2017): Distributed representations for fine-grained entity typing. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Rothe, Sascha (2017): Supervised and unsupervised methods for learning representations of linguistic units. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics

Ebert, Sebastian (2017): Artificial Neural Network methods applied to sentiment analysis. Dissertation, LMU München: Faculty for Languages and Literatures

Kaumanns, Franz David (2016): Assessment and analysis of the applicability of recurrent neural networks to natural language understanding with a focus on the problem of coreference resolution. Dissertation, LMU München: Faculty for Languages and Literatures

Sergienya, Irina (2016): Distributional initialization of neural networks. Dissertation, LMU München: Faculty for Languages and Literatures

Müller, Thomas (2015): General methods for fine-grained morphological and syntactic disambiguation. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics