Automatically Tailoring Unsupervised Morphological Segmentation to the Language
Ramy Eskander, Owen Rambow, Smaranda Muresan · 2018
Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies.Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages.Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration.However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages.We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages.