New Turkish morphology dataset based on mixed genre text used in the following paper (pdf, code).
@article{DBLP:journals/corr/abs-1805-07946,
author = {Erenay Dayanik and Ekin Aky{\"{u}}rek and Deniz Yuret},
title = {MorphNet: {A} sequence-to-sequence model that combines morphological analysis and disambiguation},
journal = {CoRR},
volume = {abs/1805.07946},
year = {2018},
url = {http://arxiv.org/abs/1805.07946},
archivePrefix = {arXiv},
eprint = {1805.07946},
timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1805-07946},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
trmor2018.train was semiautomatically annotated, randomly split (80-10-10%) five times and
the average scores were reported in the paper.
The lines in the file are either XML tags indicating sentence and document boundaries, or contain tab-separated analyses for a single word:
word<tab>tag1<tab>tag2...
The first analysis (tag1) is the correct one. When none of the analyses were deemed correct, tag1 is '?' and the other tags are printed in a random order.
trmor2018.train is verified to have 95.56% accuracy using handtagged/trmor2018.gold, a
subset that was manually annotated by two annotators with differences adjudicated by a
third. Please do not copy any data between trmor2018.train and trmor2018.gold in future
versions and do not use trmor2018.gold for training or testing models, otherwise we lose
our ability to measure accuracy using an independently tagged reference.
The analyses were produced by a newer version of Kemal Oflazer's finite state transducers circa 2018 for Turkish morphological analysis, and xfst the Xerox Finite State software. Please use both with permission. Here are the data statistics:
| trmor2018 | train |
|---|---|
| Documents | 390 |
| Sentences | 34673 |
| Tokens | 460669 |
| Unambiguous | 243866 |
| Ambiguous | 215024 |
| Unknown | 1779 |
Test set used in the following paper which used the same training set as TrMor2006.
@paper{AAAI1612370,
author = {Eray Yildiz and Caglar Tirkaz and H. Sahin and Mustafa Eren and Omer Sonmez},
title = {A Morphology-Aware Network for Morphological Disambiguation},
conference = {AAAI Conference on Artificial Intelligence},
year = {2016},
url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12370}
}
Approximately 20000 tokens from the training set were manually retagged to obtain a larger test set. Unfortunately the paper did not exclude these tokens from the training set and they did not provide any inter-annotator agreement. The data format is the same as trmor2006, here are the data statistics:
| trmor2016 | test |
|---|---|
| Documents | 42 |
| Sentences | 1286 |
| Tokens | 19262 |
| Unambiguous | 9782 |
| Ambiguous | 9446 |
| Unknown | 34 |
Turkish morphology dataset based on news text used in the following paper.
@InProceedings{yuret-ture:2006:HLT-NAACL06-Main,
author = {Yuret, Deniz and Ture, Ferhan},
title = {Learning Morphological Disambiguation Rules for Turkish},
booktitle = {Proceedings of the Human Language Technology Conference of the NAACL, Main Conference},
month = {June},
year = {2006},
address = {New York City, USA},
publisher = {Association for Computational Linguistics},
pages = {328--334},
url = {http://www.aclweb.org/anthology/N/N06/N06-1042}
}
Each line lists a token or tag followed by one or more possible lemma+tag analyses separated
by whitespace. The first analysis is the correct one. Unknown tags are marked with the
substring UNKNOWN. The analyses were produced by
tr-tagger.tgz, Kemal Oflazer's finite state
transducers circa 2006 for Turkish morphological analysis , and
xfst the Xerox Finite State software. Please use both with
permission. The training set was semi-automatically tagged and is not very accurate. The
test set was hand-tagged but is very small. Here are the data statistics:
| trmor2006 | train | test |
|---|---|---|
| Documents | 2383 | 3 |
| Sentences | 50674 | 42 |
| Tokens | 837524 | 862 |
| Unambiguous | 436406 | 482 |
| Ambiguous | 399216 | 379 |
| Unknown | 1902 | 1 |