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Turkish Morphology Datasets
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TrMor2006 README updates Feb 22, 2019
TrMor2016 README updates Feb 22, 2019
TrMor2018 compare.pl computes 95.56% accuracy for trmor2018 Feb 23, 2019
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README.md compare.pl computes 95.56% accuracy for trmor2018 Feb 23, 2019

README.md

Turkish morphology datasets

TrMor2018

New Turkish morphology dataset based on mixed genre text used in the following paper.

@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

TrMor2016

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

TrMor2006

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
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