A probabilistic approach to language structure

1. A probabilistic approach to language structure Annarita Felici and Paul Pal Royal Holloway, University of London Helsinki 2-4 June 2008 A.Felici@rhul.ac.uk P.Pal@rhul.ac.uk

2. Outline • Field of investigation • Research goals • Data • Probabilistic analysis • Information Theory • Entropy results QITL3

3. Field of investigation • Repetitive language structure in multilingual legal text • EU normative statements in translation • Languages of investigation • English, French, German and Italian QITL3

4. Field of investigation: legal norms • Deontic norms (from the Greek deon = duty).  obligations, prohibitions, permissions and authorizations • Constitutive performatives  The uttering of a performative is, or is part of, the doing of a certain kind of action or speech acts (Austin 1962) Uttering a sentence = doing things QITL3

5. Other norm types • Logical necessity  necessary requirements or competences • Non-binding norms  guidelines, correct procedure QITL3

6. Research goals • To evaluate the degree of prescriptive standardization in French, German and Italian with reference to English • To predict translation equivalents in French, German and Italian QITL3

7. under the conditions that: • English legal drafting is highly standardized • The EU and the main English drafting suggest modal verbs for prescriptive norms (Coode 1843, Driedger 1976, Dickerson 1975, Thornton 1996) • Text types under investigation are repetitive and reusable • Text types under investigation can be more or less binding QITL3

8. Data Multilingual parallel corpus • Origin: EU • Corpus size: 1.404.723 words • Text type: normative • Type of docs: Secondary Legislation(Regulations,Decisions,Directives, Recommendations) • Years:2001-04 • Languages: English, French, German, Italian QITL3

9. Probabilistic Analysis Information Theory To measure the amount of linguistic alternatives when translating a repetitive normative statement from English into French, German and Italian = Quantifying information by reducing uncertainty • more alternatives = more uncertainty (high entropy) • less alternatives = more standardization, certainty (low entropy) QITL3

10. Probabilistic Variables • Categories of expressions • Linguistic forms  English modals Entry point for parallel retrieval  shall, must, may, can, should QITL3

11. Categories of expression • Constitutive norms and performatives • Logical necessity • Permissions and authorizations • Capability • Non-binding norms QITL3

12. Linguistic forms • Indicative (pres.) • Modal verbs (mv) • Verbal periphrasis (vp) • Lexicalized modal expressions (le) • Ellipses (0- correspondence) QITL3

13. Linguistic formsLinguistic equivalents used in constitutive and performative norms QITL3

14. Linguistic formsLinguistic equivalents used to convey permissions and authorizations QITL3

15. Given the English system of modality, which is the relative probability of choosing an equivalent modal verb in the translation of may or must and a different linguistic form as the equivalent of shall? • Is the probability of a choice in a system affected by a choice in another? QITL3

16. Information Theory • the information value or content h(p) is dependent on the probability of occurrence (p) of an event (Shannon 1949) h(p)= - log (p) = log (1/p) Entropy degree of uncertainty (= shortage of information due to the large number of alternatives) QITL3

17. Probabilistic analysis • The frequency of occurrence (ni) of each linguistic form is associated with a category • A probability variable (pi) is derived from the estimated proportion of a particular linguistic form QITL3

18. Probabilistic analysis • In English P1 = p mv→ shall = n shall/ n; p2 = pmv → must = nmust/ n; p3 = pmv →should = nshould/n; p4 =pmv → can = ncan/n; p5 = pmv → may = nmay/ n • In French, German and Italian p1 = pindicative + pmv + pvp + pme + pellipses; p2 = pindicative + pmv + pvp + pme + pellipses and so on. QITL3

19. Linguistic forms and frequencies of occurrences in the EU Regulation for the selected categories of 1) constitutive norms and 2) permissions and authorization QITL3

20. Probabilistic approach • The sum of these probabilities produces different information values • The expected information content of a system is the sum of the information contents weighted by the probabilities for each possible outcome QITL3

21. Entropy : extrema • Variations in the language-specific p(i) values of linguistic forms produce distribution profiles reflecting the characteristics of the corresponding language. • Mathematically it can be shown that If all the p(i) values are equal (equi-probable situation), the profile is a uniform distribution and results in maximum entropy. If only one probability p(i) is maximum and the remaining p(i) values are zero, the entropy is minimum (e.g. English). All other distributions lie between these two limits (e.g. French, German and Italian) QITL3

22. A concrete example • Regulation document in English, French, German and Italian + a fictitious language. • One category of expression: e.g. the constitutive norms. • 5 linguistic forms for this category. • Total number of modal verbs and alternatives: 2075. QITL3

23. Constitutive norm Frequency of occurrences of expression modes in 4 real languages and one fictitious language QITL3

24. Histogram of 5 modes of expression QITL3

25. Comparison based on Entropy Computed Entropy of Constitutive norm EN H = 0 + Hmv + 0 + 0 + 0 = 0.405 FR H = Hind + Hmv + Hvp + Hme + Hme =0.857 GE H = Hind + Hmv + Hvp + Hme + Hme =1.08 IT H = Hind + Hmv + Hvp + Hme + Hme =0.88 FI H = Hind + Hmv + Hvp + Hme + Hme =2.32 QITL3

26. Computed Entropy of constitutive norms (English, French, German, Italian and Fictitious) QITL3

27. Entropy results • In the EU Regulation according to the 5 categories of expression (1. Constitutive and performative norms, 2. Logical necessity, 3.Permissions and authorizations, 4.Capability, 5. Non-binding norms) • In the EU Secondary Legislation overall according to the 4 types of documents (Regulations, Decisions, Directives, Recommendations) QITL3

28. Entropy in the EU Regulation QITL3

29. Entropy resultsEU Regulation • Logical necessity, permissions and authorizations and capability(< entropy) • quite standardized in the 4 languages = almost equivalent translations • Constitutive performative norms(> entropy) • translation is more difficult to predict • Definitions, const. statements, obligations • FR: < entropy than IT • DE: > entropy (VP sein/haben…zu) QITL3

30. Entropy resultsEU Regulation • Non -binding norms • fairly amount of variation among the 4 languages • FR/IT: >entropy • DE: < entropy (should is most likely translated with sollen- Soll-Vorschriften) QITL3

31. Entropy overall the 4 EU documents QITL3

32. Entropy resultsEU Secondary Legislation • Regulations and Decisions(< entropy) • Direct applicability of the norms = more precision and standardization • FR looks more standardized than IT and DE • Directives(> entropy than Reg. and Dec.) • Binding only as to the result to be achieved • Recommendations (> entropy) • Not-binding: more freedom • DE : sollen QITL3

33. Conclusions • Given certain conditions, it is possible to predict with some certainty the occurrence of a particular factor • If applied to repetitive texts, entropy analysis can enhance research in langauge testing, evaluation and in the development of automated translation’s tools QITL3

34. References • Austin, J. L. 1962. How to do things with words.Oxford: Oxford University Press. • Coode, G. 1843. Legislative Expressions. Appendix to the Report of the Poor Law Commissioners on Local Taxation. Published separately 1845, 2nd Ed.1852. • Driedger, E. A. 1976. The Composition of legislation. Legislative forms and precedents(2nd Ed.). Ottawa:The Department of Justice • Shannon, Cand W. Weaver. 1963 (1949) The mathematical theory of communication. Urbana: University of Illinois Press.USA. • Thornton G.C. 1996. Legislative Drafting (4th Ed.). Butterworths, London. • http://publications.europa.eu/code/en/en-6000000.htm QITL3