no code implementations • LT4HALA (LREC) 2022 • Oliver Hellwig, Sven Sellmer
Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research.
no code implementations • LT4HALA (LREC) 2022 • Sebastian Nehrdich, Oliver Hellwig
Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research.
1 code implementation • WS 2020 • Ashim Gupta, Amrith Krishna, Pawan Goyal, Oliver Hellwig
Neural sequence labelling approaches have achieved state of the art results in morphological tagging.
no code implementations • LREC 2020 • Oliver Hellwig
This paper introduces and evaluates a Bayesian mixture model that is designed for dating texts based on the distributions of linguistic features.
no code implementations • LREC 2020 • Oliver Hellwig, Salvatore Scarlata, Elia Ackermann, Paul Widmer
This paper introduces the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language that is of central importance for linguistic and historical research.
no code implementations • EMNLP 2018 • Oliver Hellwig, Sebastian Nehrdich
The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi).
no code implementations • COLING 2016 • Wiebke Petersen, Oliver Hellwig
The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix.
no code implementations • WS 2016 • Oliver Hellwig
The paper describes a new tagset for the morphological disambiguation of Sanskrit, and compares the accuracy of two machine learning methods (Conditional Random Fields, deep recurrent neural networks) for this task, with a special focus on how to model the lexicographic information.
no code implementations • COLING 2016 • Oliver Hellwig
The paper applies a deep recurrent neural network to the task of sentence boundary detection in Sanskrit, an important, yet underresourced ancient Indian language.