Kevin Black


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Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models
Paul Felt | Kevin Black | Eric Ringger | Kevin Seppi | Robbie Haertel
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
Kevin Black | Eric Ringger | Paul Felt | Kevin Seppi | Kristian Heal | Deryle Lonsdale
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The task of corpus-dictionary linkage (CDL) is to annotate each word in a corpus with a link to an appropriate dictionary entry that documents the sense and usage of the word. Corpus-dictionary linked resources include concordances, dictionaries with word usage examples, and corpora annotated with lemmas or word-senses. Such CDL resources are essential in learning a language and in linguistic research, translation, and philology. Lemmatization is a common approximation to automating corpus-dictionary linkage, where lemmas are treated as dictionary entry headwords. We intend to use data-driven lemmatization models to provide machine assistance to human annotators in the form of pre-annotations, and thereby reduce the costs of CDL annotation. In this work we adapt the discriminative string transducer DirecTL+ to perform lemmatization for classical Syriac, a low-resource language. We compare the accuracy of DirecTL+ with the Morfette discriminative lemmatizer. DirecTL+ achieves 96.92% overall accuracy but only by a margin of 0.86% over Morfette at the cost of a longer time to train the model. Error analysis on the models provides guidance on how to apply these models in a machine assistance setting for corpus-dictionary linkage.