Parsing of Czech: Between Rules and Stats

​IA161 Advanced NLP Course?, Course Guarantee: Aleš Horák

Prepared by: Miloš Jakubíček

State of the Art

References

1. Zhang, Y., Zhou, H., & Li, Z. (2020). Fast and Accurate Neural CRF Constituency Parsing. arXiv preprint arXiv:2008.03736.
2. Qi, P., Dozat, T., Zhang, Y., & Manning, C. D. (2019). Universal dependency parsing from scratch. arXiv preprint arXiv:1901.10457.
3. Straka, M., Straková, J., & Hajič, J. (2019). Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER. In International Conference on Text, Speech, and Dialogue (pp. 137-150). Springer, Cham.
4. Baisa, V. and Kovář, V. (2014). Information extraction for Czech based on syntactic analysis. In Vetulani, Z. and Mariani, J., editors,Human Language Technology Challenges for Computer Science and Linguistics, pages 155–165. Springer International Publishing.

Practical Session

We will develop/adjust the grammar of the SET parser.

1. Download the SET parser with evaluation dataset

   wget https://nlp.fi.muni.cz/trac/research/chrome/site/bigdata/ukol_ia161-parsing.zip
   

2. Unzip the downloaded file

   unzip ukol_ia161-parsing.zip
   

3. Go to the unziped folder

   cd ukol_ia161-parsing
   

4. Test the prepared program that analyses 100 selected sentences

   make set_trees
   make compare
   

   The output should be

   ./compare_dep_trees.py data/trees/pdt2_etest data/trees/set_pdt2_etest-sel100
   UAS =  66.1 %
   

   You can see detailed evaluation (sentence by sentence) with

   make compare SENTENCES=1
   

   You can watch differences for one tree with

   make diff SENTENCE=00009
   

   Exit the diff by pressing q.
   You can watch the two trees with (python-qt4 must be installed in the system)

   make view SENTENCE=00009
   

   For remote tree view, you may run

   make html SENTENCE=00009
   

   And point your browser to the html/index.html file.
   You can extract the text of the sentence (e.g. for Google translate) easily with

   make text SENTENCE=00009
   

5. Look at the files:
   • data/vert/pdt2_etest-sel100 - 100 input sentences in vertical format. The tag format is the Prague Dependency Treebank ​positional tagset
   • data/trees/pdt2_etest - 100 gold standard dependency trees from the Prague Dependency Treebank
   • data/trees/set_pdt2_etest-sel100 - 100 trees output from SET by running make set_trees
   • grammar.set - the grammar used in running SET

Assignment

1. Study the ​SET documentation. The tags used in the grammar are in the Brno tagset.
2. Develop better grammar - repeat the process:

   edit grammar.set # use your favourite editor
   make set_trees
   make compare
   

   to improve the original UAS
3. Write the final UAS in grammar.set

   # This is the SET grammar for Czech used in IA161 course
   # 
   # ===========   resulting UAS =  66.1 %  ===================
   

4. Upload your grammar.set to the homework vault.