1. HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE ADAPTIVE INFORMATICS RESEARCH CENTRE Unsupervised Segmentation of Words into Morphemes Morpho Challenge Workshop 2006 Mikko Kurimo, Mathias Creutz, Krista Lagus

2. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Opening – Welcomes Welcome to the Morphochallenge workshop, everybody! challenge participants workshop speakers other PASCAL researchers others interested in the topic

3. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Motivation To design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Get basic vocabulary units suitable for different tasks: Speech and text understanding Machine translation Information retrieval Statistical language modelling Rule based systems can split: read + ing, but have difficulties for complicated words and languages

4. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Workshop 12 April, final timetable 0900 Opening 0910 Introduction and evaluation report 0950 Invited talk by Richard Sproat 1050 Break 1120 Morfessor baseline by Krista Lagus 1150 Competitors presentations 1230 Lunch 1400 Competitors (contd.) 1500 Discussion 1530 Conclusion

5. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Morning session 09:10 Mikko Kurimo Introduction and Evaluation report 09:50 Prof. Richard Sproat (Invited Talk) University of Illinois at Urbana-Champaign ”Computational Morphology and its Implications for the Theoretical Morphology” 10:50 – 11:20 Coffee break

6. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Noon session 11:20 Krista Lagus: "Morfessor in MorphoChallenge" 11:50 Delphine Bernhard: "Morphological segmentation for the automatic acquisition of semantic relationships in the context of MorphoChallenge 2005" 12:10 Stefan Bordag: "Two-step approach to unsupervised morpheme segmentation" 12:30 – 14:00 Lunch

7. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Afternoon session 14:00 Lars Johnsen: "Learning morphology on tokens" 14:20 Samarth Keshava and Emily Pitler: "Reports - Quick and Simple Unsupervised Learning of Morphemes" 14:40 Eric Atwell (Mikko Kurimo): "Combinatory Hybrid Elementary Analysis of Text" 15:00 Discussion 15:30 Conclusion

8. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Discussion topics for afternoon New ways to evaluate the obtained units ? New evaluation languages: German, Norwegian, French, Estonian, Arabic,..? Other application evaluations: SLU, IR, MT,..? New organizer partners ? MorphoChallenge2 ? Journal special issue ? 2nd Morpho Challenge workshop ? ?

9. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Opening - Thanks Thanks to all who made Morpho Challenge possible! PASCAL network, coordinators, challenge program organizers Morpho Challenge organizing committee Morpho Challenge program committee Morpho Challenge participants Morpho Challenge evaluation team Challenge workshop organizers

10. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Let’s start. It is my pleasure to welcome the first speaker, who is...

11. HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE ADAPTIVE INFORMATICS RESEARCH CENTRE Morpho Challenge – Introduction and evaluation report Mikko Kurimo, Mathias Creutz, Matti Varjokallio (Helsinki, FI) Ebru Arisoy, Murat Saraclar (Istanbul, TR)

12. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Contents 1.Motivation 2.Call for participation 3.Rules 4.Datasets 5.Participants 6.Results of competition 1, word segmentation 7.Results of competition 2, language modeling 8.Conclusion

13. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Motivation To design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Get basic vocabulary units suitable for different tasks: Speech and text understanding Machine translation Information retrieval Statistical language modelling

14. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Motivation The scientific goals of this challenge are: To learn of the phenomena underlying word construction in natural languages To discover approaches suitable for a wide range of languages To advance machine learning methodology

15. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Contents 1.Motivation 2.Call for participation 3.Rules 4.Datasets 5.Participants 6.Results of competition 1, word segmentation 7.Results of competition 2, language modeling 8.Conclusion

16. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Call for participation Part of the EU Network of Excellence PASCAL’s Challenge Program Participation is open to all and free of charge Word sets are provided for three languages: Finnish, English, and Turkish Implement an unsupervised algorithm that segments the words of each language! No language-specific tweaking parameters, please Write a paper that describes your algorithm

17. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Rules Segmented words are submitted to the organizers Two different evaluations are made Competition 1: Comparison to a linguistic morpheme segmentation "gold standard“ Competition 2: Speech recognition experiments, where statistical n-gram language models utilize the morphemes instead of entire words.

18. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Datasets Word lists are downloadable at our home page Each word in the list is preceded by its frequency Finnish: newspapers, books, newswires: 1.6/32M Turkish: web, newspapers, sports news: 0.6/17M English: Gutenberg, Gigaword, Brown: 170k/24M Small gold standard sample in each language

19. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Participants A1 Choudri and Dang, Univ. Leeds, UK A2 a,b, Bernhard, TIMC-IMAG, F A3 'A.A.‘ Ahmad and Allendes, Univ. Leeds, UK A4 ‘comb’,’lsv’, Bordag, Univ. Leipzig, D A5 Rehman and Hussain, Univ. Leeds, UK A6 'RePortS‘, Pitler and Keshava, Univ. Yale, USA A7 Bonnier, Univ. Leeds, UK A8 Kitching and Malleson, Univ. Leeds, UK A9 'Pacman‘, Manley and Williamson, Univ. Leeds, UK A10 Johnsen, Univ. Bergen, NO A11 'Swordfish‘, Jordan, Healy and Keselj, Univ. Dalhousie, CA A12 'Cheat‘, Atwell and Roberts, Univ. Leeds, UK M1-3 Morfessor, Categories-ML, MAP, Helsinki Univ. Tech, FI

20. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Contents 1.Motivation 2.Call for participation 3.Rules 4.Datasets 5.Participants 6.Results of competition 1, word segmentation 7.Results of competition 2, language modeling 8.Conclusion

21. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Competition 1: Word segmentation Two samples : boule_vard, cup_bearer_s‘ Gold standard: boulevard, cup_bear_er_s_‘ 2 correct hits (H), 1 insertion (I), 2 deletions (D) Precision = H / (H + I) = 2 / (2 + 1) = 0.67 Recall = H / (H + D) = 2 / (2 + 2) = 0.50 F-Measure = harmonic mean of precision and recall = 2H / (2H + I + D) = 4 / (4 + 1 + 2) = 0.57 A secret (random)10% subset of words evaluated Morfessor Baseline: 54.2% FI, 51.3% TR, 66.0 EN

22. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Results: F-measure in Finnish data

23. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure with reference algorithms

24. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure in Turkish data

25. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure with reference algorithms

26. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure in English data

27. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure with reference algorithms

28. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE F-measure, the 3 languages task

29. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE...with reference algorithms

30. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Contents 1.Motivation 2.Call for participation 3.Rules 4.Datasets 5.Participants 6.Results of competition 1, word segmentation 7.Results of competition 2, language modeling 8.Conclusion

31. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Competition 2: Language modeling A statistical N-gram LM trained for the obtained morphemes using a large text corpus Growing N-gram model for Finnish by HUT tools 4-gram model for Turkish using SRILM Free lexicon size (40´000 – 700´000) ~10M N-grams (Finnish) or 50-70M bytes (Turkish)

32. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Evaluation by speech recognition Realistic benchmark application: Continuous reading of large-vocabulary texts (books and news) Letter error rate LER% = (sub + ins + del) / letters Baseline systems using LMs of Morfessor’s segments Finnish recognizer made at HUT (HUT tools): speaker- dep., running speed 10-15 xRT, baseline 1.31% LER Turkish made at Bogazici Univ. (HTK and AT&T tools): speaker-indep., running 2-3 xRT, baseline 13.7% LER

33. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Speech recognition letter error rate (LER)

34. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE LER for reference algorithms

35. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE LER for grammatic rules and words, too

36. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Update for Turkish results NEW

37. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Contents 1.Motivation 2.Call for participation 3.Rules 4.Datasets 5.Participants 6.Results of competition 1, word segmentation 7.Results of competition 2, language modeling 8.Conclusion

38. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Conclusion The scientific goals of this challenge are: To learn of the phenomena underlying word construction in natural languages To discover approaches suitable for a wide range of languages To advance machine learning methodology

39. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Conclusion 14 different unsupervised segmentation algorithms 12 participating research groups Evaluations for 3 languages Full report and papers in the proceedings Website: http://www.cis.hut.fi/morphochallenge2005

40. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Acknowledgments Text and speech data providers in all languages! Finnish and Turkish evaluation teams Funding from PASCAL, Finnish Academy, Lang. Tech. Grad school, HUT, and Bogazici Univ. LM and ASR tools in HUT, SRI, and AT&T Competition participants!

41. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE The second speaker today : Professor Richard Sproat, University of Illinois at Urbana-Champaign: ”Computational Morphology and its Implications for the Theoretical Morphology”

42. HELSINKI UNIVERSITY OF TECHNOLOGY ADAPTIVE INFORMATICS RESEARCH CENTRE Richard Sproat Professor of Linguistics and Electrical and Computer Engineering at the University of Illinois and head of the Computational Linguistics Lab at the Beckman Institute. Received his Ph.D. from MIT in 1985 and has since then worked also at AT&T Bell Labs. A well-known expert in language and computational linguistics, including syntax, morphology, computational morphology, articulatory and acoustic phonetics, text processing, text-to- speech synthesis, writing systems, and text-to-scene conversion.