1. Near Language Identification Using NooJ Božo Bekavac, Kristina Kocijan, Marko Tadić Faculty of Humanities and Social Sciences University of Zagreb, Croatia NooJ 2014 Sassari 2014-06-04

2. Introduction  It is not hard to distinguish automatically very different languages, but similar languages like  Czech, Slovakian  Indonesian, Malaysian or  Brazilian Portuguese, European Portuguese  is very hard to distinguish even for state-of-the-art statistical tools  they often mix those languages  We use NooJ as a core part of a system designed for automatic identification of near languages  Croatian and Serbian

3. Differences: Croatian - Serbian  Lexical level (some differences)  Reflex of proto-Slavic vowel jat ije/je vs. e  e. g. milk (en) –mlijeko (hr) vs. mleko (sr)  verbs ending –irati, - ovati  e. g. to employ (en) – angažirati (hr) vs. angažovati (sr)  Construction of future tense  analytical in hr, e. g. pitat ću (I will ask)  synthetic in sr, e. g. pitaću (I will ask)  Typical structures for certain language  Croatian: modal verb + infinitive, e. g. hoću raditi  Serbian: modal verb + da + present, e. g. hoću da radim NooJ2014 Sassari 2014-06-04

4. Formalizing differences  We used only Croatian language resources  and designed morphological grammars for recognition of unknown tokens in Serbian  some words specific to Serbian are left unknown ( e. g. bread (en) – kruh (hr) vs. hleb (sr)  but it had no impact on efficiency of system  Syntactic and lexical grammars focuses on formalization of differences between languages  Examples follow… NooJ2014 Sassari 2014-06-04

5. Lexical grammars (1)  E. g. president (en) –predsjednik (hr) vs. predsednik (sr) NooJ2014 Sassari 2014-06-04

6. Lexical grammars (2)  E. g. to meet (en) –sastati (hr) vs. sastaću (sr) NooJ2014 Sassari 2014-06-04

7. Syntactic grammars (sr)  E. g. should do (en) - treba da uradi (sr) NooJ2014 Sassari 2014-06-04

8. Syntactic grammars (hr)  E. g. should do (en) - treba uraditi (sr) NooJ2014 Sassari 2014-06-04

9. Implementation  Instead of NoojApply we applied:  Fully automated process through Autohotkey  http://www.autohotkey.com/ http://www.autohotkey.com/  AutoHotkey - a scripting language for desktop automation > Max suggested  enables emulation of clicking on desktop applications  enables scripting language capabilities  Pros & cons are discussed in conclusion NooJ2014 Sassari 2014-06-04

10. System description  Open text  Apply Croatian language linguistic analyses  Count  No. of tokens  No. of Serbian lng. lexical units  No. of syntactic constructions V da V  No. of syntactic constructions V Vinf  Make decision in respect to obtained results from above processing  based on percentages of occurrences  Write statistics and results NooJ2014 Sassari 2014-06-04

11. Output of processing  Demo NooJ2014 Sassari 2014-06-04

12. Results  Testing was performed on corpus of 2500 articles from SETimes corpus  http://www.setimes.com/ http://www.setimes.com/  texts on Serbian and Croatian language  short news translated from English  System obtained precision of 99,82 %  Outperforming all known systems in this task  3 texts on Serbian language are misclassified as Croatian  texts with low recall in considered criteria NooJ2014 Sassari 2014-06-04

13. Conclusion & future work  NooJ and AutoHotkey in combination are sufficient even for performing very complex tasks  The system is completely automatized  Disadvantage: AutoHotkey is very dependent on computer screen resolution (automatic clicking)  Future work:  There is room for improvement of the system  To take into account unknown words  To tune system voting  To create lists of „forbidden” words NooJ2014 Sassari 2014-06-04

14. Thank you for your attention!