Jordan Lachler


2019

pdf bib
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz | Miikka Silfverberg
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2018

pdf bib
Modeling Northern Haida Verb Morphology
Jordan Lachler | Lene Antonsen | Trond Trosterud | Sjur Moshagen | Antti Arppe
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

pdf bib
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages
Antti Arppe | Jeff Good | Mans Hulden | Jordan Lachler | Alexis Palmer | Lane Schwartz
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

pdf bib
A Morphological Parser for Odawa
Dustin Bowers | Antti Arppe | Jordan Lachler | Sjur Moshagen | Trond Trosterud
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages

2016

pdf bib
Training & Quality Assessment of an Optical Character Recognition Model for Northern Haida
Isabell Hubert | Antti Arppe | Jordan Lachler | Eddie Antonio Santos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We are presenting our work on the creation of the first optical character recognition (OCR) model for Northern Haida, also known as Masset or Xaad Kil, a nearly extinct First Nations language spoken in the Haida Gwaii archipelago in British Columbia, Canada. We are addressing the challenges of training an OCR model for a language with an extensive, non-standard Latin character set as follows: (1) We have compared various training approaches and present the results of practical analyses to maximize recognition accuracy and minimize manual labor. An approach using just one or two pages of Source Images directly performed better than the Image Generation approach, and better than models based on three or more pages. Analyses also suggest that a character’s frequency is directly correlated with its recognition accuracy. (2) We present an overview of current OCR accuracy analysis tools available. (3) We have ported the once de-facto standardized OCR accuracy tools to be able to cope with Unicode input. Our work adds to a growing body of research on OCR for particularly challenging character sets, and contributes to creating the largest electronic corpus for this severely endangered language.

2014

pdf bib
Modeling the Noun Morphology of Plains Cree
Conor Snoek | Dorothy Thunder | Kaidi Lõo | Antti Arppe | Jordan Lachler | Sjur Moshagen | Trond Trosterud
Proceedings of the 2014 Workshop on the Use of Computational Methods in the Study of Endangered Languages