5 research outputs found
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources
We propose a novel hybrid approach to lemmatization that enhances the seq2seq
neural model with additional lemmas extracted from an external lexicon or a
rule-based system. During training, the enhanced lemmatizer learns both to
generate lemmas via a sequential decoder and copy the lemma characters from the
external candidates supplied during run-time. Our lemmatizer enhanced with
candidates extracted from the Apertium morphological analyzer achieves
statistically significant improvements compared to baseline models not
utilizing additional lemma information, achieves an average accuracy of 97.25%
on a set of 23 UD languages, which is 0.55% higher than obtained with the
Stanford Stanza model on the same set of languages. We also compare with other
methods of integrating external data into lemmatization and show that our
enhanced system performs considerably better than a simple lexicon extension
method based on the Stanza system, and it achieves complementary improvements
w.r.t. the data augmentation method
Computational Etymology: Word Formation and Origins
While there are over seven thousand languages in the world, substantial language technologies exist only for a small percentage of these. The large majority of world languages do not have enough bilingual or even monolingual data for developing technologies like machine translation using current approaches. The computational study and modeling of word origins and word formation is a key step in developing comprehensive translation dictionaries for low-resource languages. This dissertation presents novel foundational work in computational etymology, a promising field which this work is pioneering. The dissertation also includes novel models of core vocabulary, dictionary information distillation, and of the diverse linguistic processes of word formation and concept realization between languages, including compounding, derivation, sense-extension, borrowing, and historical cognate relationships, utilizing statistical and neural models trained on the unprecedented scale of thousands of languages. Collectively these are important components in tackling the grand challenges of universal translation, endangered language documentation and revitalization, and supporting technologies for speakers of thousands of underserved languages