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Found 13 papers, 1 papers with code
Detecting Diachronic Syntactic Developments in Presence of Bias Terms
Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research.
Accurate Dependency Parsing and Tagging of Latin
Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research.
Evaluating Neural Morphological Taggers for Sanskrit
Neural sequence labelling approaches have achieved state of the art results in morphological tagging.
Dating and Stratifying a Historical Corpus with a Bayesian Mixture Model
This paper introduces and evaluates a Bayesian mixture model that is designed for dating texts based on the distributions of linguistic features.
The Treebank of Vedic Sanskrit
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.
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks
The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi).
Improving the Morphological Analysis of Classical Sanskrit
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.
Detecting Sentence Boundaries in Sanskrit Texts
The paper applies a deep recurrent neural network to the task of sentence boundary detection in Sanskrit, an important, yet underresourced ancient Indian language.
Exploring the value space of attributes: Unsupervised bidirectional clustering of adjectives in German
The paper presents an iterative bidirectional clustering of adjectives and nouns based on a co-occurrence matrix.
