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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage
%A Black, Kevin
%A Ringger, Eric
%A Felt, Paul
%A Seppi, Kevin
%A Heal, Kristian
%A Lonsdale, Deryle
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F black-etal-2014-evaluating
%X 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.
%U https://aclanthology.org/L14-1142/
%P 3798-3805

Markdown (Informal)

[Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage](https://aclanthology.org/L14-1142/) (Black et al., LREC 2014)

• Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage (Black et al., LREC 2014)

ACL

• Kevin Black, Eric Ringger, Paul Felt, Kevin Seppi, Kristian Heal, and Deryle Lonsdale. 2014. Evaluating Lemmatization Models for Machine-Assisted Corpus-Dictionary Linkage. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3798–3805, Reykjavik, Iceland. European Language Resources Association (ELRA).