Andrew Cowell


2020

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Cross-lingual annotation: a road map for low- and no-resource languages
Meagan Vigus | Jens E. L. Van Gysel | Tim O’Gorman | Andrew Cowell | Rosa Vallejos | William Croft
Proceedings of the Second International Workshop on Designing Meaning Representations

This paper presents a “road map” for the annotation of semantic categories in typologically diverse languages, with potentially few linguistic resources, and often no existing computational resources. Past semantic annotation efforts have focused largely on high-resource languages, or relatively low-resource languages with a large number of native speakers. However, there are certain typological traits, namely the synthesis of multiple concepts into a single word, that are more common in languages with a smaller speech community. For example, what is expressed as a sentence in a more analytic language like English, may be expressed as a single word in a more synthetic language like Arapaho. This paper proposes solutions for annotating analytic and synthetic languages in a comparable way based on existing typological research, and introduces a road map for the annotation of languages with a dearth of resources.

2019

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Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2018

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A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer
Sarah Moeller | Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

We experiment with training an encoder-decoder neural model for mimicking the behavior of an existing hand-written finite-state morphological grammar for Arapaho verbs, a polysynthetic language with a highly complex verbal inflection system. After adjusting for ambiguous parses, we find that the system is able to generalize to unseen forms with accuracies of 98.68% (unambiguous verbs) and 92.90% (all verbs).

2017

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Creating lexical resources for polysynthetic languages—the case of Arapaho
Ghazaleh Kazeminejad | Andrew Cowell | Mans Hulden
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

2016

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Applying Universal Dependency to the Arapaho Language
Irina Wagner | Andrew Cowell | Jena D. Hwang
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

2006

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Retrospective Analysis of Communication Events - Understanding the Dynamics of Collaborative Multi-Party Discourse
Andrew Cowell | Jereme Haack | Adrienne Andrew
Proceedings of the Analyzing Conversations in Text and Speech