A probabilistic approach to language structure
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A probabilistic approach to language structure. Annarita Felici and Paul Pal Royal Holloway, University of London Helsinki 2-4 June 2008 [email protected]@rhul.ac.uk. Outline. Field of investigation Research goals Data Probabilistic analysis Information Theory Entropy results.

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A probabilistic approach to language structure

A probabilistic approach to language structure

Annarita Felici and Paul Pal

Royal Holloway, University of London

Helsinki 2-4 June 2008

[email protected]@rhul.ac.uk


Outline

Outline

  • Field of investigation

  • Research goals

  • Data

  • Probabilistic analysis

  • Information Theory

  • Entropy results

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Field of investigation

Field of investigation

  • Repetitive language structure in multilingual legal text

  • EU normative statements in translation

  • Languages of investigation

    • English, French, German and Italian

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Field of investigation legal norms

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

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Other norm types

Other norm types

  • Logical necessity

     necessary requirements or competences

  • Non-binding norms

     guidelines, correct procedure

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Research goals

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

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A probabilistic approach to language structure

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

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A probabilistic approach to language structure

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

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Probabilistic analysis

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)

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Probabilistic variables

Probabilistic Variables

  • Categories of expressions

  • Linguistic forms

     English modals Entry point for parallel retrieval

    shall, must, may, can, should

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Categories of expression

Categories of expression

  • Constitutive norms and performatives

  • Logical necessity

  • Permissions and authorizations

  • Capability

  • Non-binding norms

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Linguistic forms

Linguistic forms

  • Indicative (pres.)

  • Modal verbs (mv)

  • Verbal periphrasis (vp)

  • Lexicalized modal expressions (le)

  • Ellipses (0- correspondence)

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Linguistic forms linguistic equivalents used in constitutive and performative norms

Linguistic formsLinguistic equivalents used in constitutive and performative norms

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Linguistic forms linguistic equivalents used to convey permissions and authorizations

Linguistic formsLinguistic equivalents used to convey permissions and authorizations

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A probabilistic approach to language structure

  • 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?

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Information theory

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)

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Probabilistic analysis1

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

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Probabilistic analysis2

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.

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A probabilistic approach to language structure

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

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Probabilistic approach

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

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Entropy extrema

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)

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A concrete example

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.

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Constitutive norm

Constitutive norm

Frequency of occurrences of expression modes in 4 real languages and one fictitious language

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Histogram of 5 modes of expression

Histogram of 5 modes of expression

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Comparison based on entropy

Comparison based on Entropy

Computed Entropy of Constitutive norm

ENH = 0 + Hmv + 0 + 0 + 0 = 0.405

FRH = Hind + Hmv + Hvp + Hme + Hme =0.857

GEH = Hind + Hmv + Hvp + Hme + Hme =1.08

ITH = Hind + Hmv + Hvp + Hme + Hme =0.88

FIH = Hind + Hmv + Hvp + Hme + Hme =2.32

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Computed entropy of constitutive norms english french german italian and fictitious

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

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Entropy results

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)

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Entropy in the eu regulation

Entropy in the EU Regulation

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Entropy results eu regulation

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)

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Entropy results eu regulation1

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)

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Entropy overall the 4 eu documents

Entropy overall the 4 EU documents

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Entropy results eu secondary legislation

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

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Conclusions

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

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References

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

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