3 research outputs found
Trance parser model for Korean: Sejong treebank
<p>Trance parsing model + Embedding vector.</p>
<p>See https://github.com/tarowatanabe/trance for the parser and its usage. We also provide parsing and learning scripts for the Trance parser that we used for the paper; </p>
<p>1/ parsing model: ptb_train.txt.model-d100.tar.gz </p>
<p>2/ embedding vector: embedding-d100.vec.gz</p>
<p>3/ trance parser parsing script: trance-parsing.sh</p>
<p>4/ trance parser (batch) learning script: trance-training-batch.sh</p>
<p>5/ test.txt (gold file) and test.txt.leaf is for the parser input. </p>
<p>## Jungyeul Park, A Note on Constituent Parsing for Korean Using the Sejong Treebank (submitted to TALLIP). October 2017.</p>
<p>See https://github.com/jungyeul/tallip-sjtree-parsing for more detail. </p
MaltParser model for Korean: Sejong treebank
<p>MaltParser model for Korean: Sejong treebank</p>
<p>Jungyeul Park, Jeen-Pyo Hong, and Jeong-Won Cha (2016). Korean Language Resources for Everyone. In Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation (PACLIC 30). Seoul, Korea. [pdf]</p>
<p>@inproceedings{park-hong-cha:2016:PACLIC, <br>
address = {Seoul, Korea}, <br>
author = {Park, Jungyeul and Hong, Jeen-Pyo and Cha, Jeong-Won}, <br>
booktitle = {Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation (PACLIC 30)}, <br>
pages = {49--58}, <br>
title = {{Korean Language Resources for Everyone}}, <br>
year = {2016} <br>
}</p>
<p>It requires Espresso's POS tagging results for input. Espresso is available at https://zenodo.org/record/884606 </p
Sentence Boundary Data Set for French
<p>Jungyeul Park (in preparation). New Approaches for Sentence Boundary Detection - From data curation to building applications for French. Intended to submit to Language Resources and Evaluation, Springer. (May, 2018); </p>
<p> </p>
<p>See also https://github.com/jungyeul/french-sbd</p>
<p> </p