Formal Models for a Legislative Grammar. Explicit Text Amendment Andrea Bolioli, Pietro Mercatali,...

Formal Models for a Legislative Grammar.

Explicit Text AmendmentAndrea Bolioli, Pietro Mercatali, Francesco Romano

CONSIGLIO NAZIONALE DELLE RICERCHEIstituto di Teoria e Tecniche dell’Informazione Giuridica

KMGov 2004Krems, 19 may 2004

francesco

Good morning my name is Francesco Romano, now I will present a project of my institute (Institute of theory and tecnichs for legal information) aimed to the compilation of a legislative grammar.

Krems 19 may 2004 2

Scope and Assumptions 1

For the communication of legislative sources through the Internet, the parliamentary and governmental institutions of many countries have begun a process of converting their “deposits” of these, into a standard format


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• The XML mark-up language seems to be the tool deputised for reaching this scope.

• This language combining its dual nature as a mark-up language and a Web standard, is able to form the common ground for action both “at the source”, namely, legislative drafting, and action “downstream” relating to the publication of the texts and the identification of tools for accessing legislative information


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francesco

francesco13/05/2004In Italy, the introduction of the XML language, for processing legislative instruments was proposed and experimented in the “Norme in rete” [Law on the Net] Project, solicited by the Ministry of Justice, financed by CNIPA [Agency for Informatics in the Public Administration] and developed under the guidance of my institute.

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• In order to adopt this language as a standard and, above all, for the conversion of the legislative instruments in force into the format provided for, by the DTD rules, two factors, in our opinion, must interact:

• A) Definition and promotion of a “controlled” legislative language

• B) Use of tools for natural language recognition


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francesco

Infact, the rules for law-making, or techniques for legislative drafting, have introduced recurrent elements into legislative instruments, whereby it is possible to identify a more controlled language in legislative language, compared to natural language. And the presence of common rules, consolidate the definition of text models and assists in the automated recognition of the structures of legislative instruments.

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The Method

For this research, the methodological approach can be subdivided into the following steps:

• identification of the technical tool for the implementation of the parser and the extraction of the information;

• identification and description of the models, on the basis of legal rules;

• identification and description of the textual structures expressing the defined legal models;

• choice of the sample of legislative instruments to be analysed, compilation of the grammar according to the syntax of the pre-selected parser and the automated analysis of the sample.


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francesco

Now we shall attempt to define and describe these phases.

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Technical Tool for the Implementation of the Parser and for Information Extraction (1)The suitable tool for the recognition and tagging of a legislative instrument, has been identified in the Sophia 2.1 system of parsing.

In particular, we are working with this software on analysing and tagging the first sample of legislative instruments, in the following phases:

• normalisation of the entry text, properly tagging all those structures and textual segments that can be recognised on the basis of characters or, in other words, without resort to or consultation of the lexicon-dictionary;

• lexical (syntactical category) and morphological (flexion passages) analysis of the text in input;


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francesco

The first phase is identification of the technical tool for the implementation of the parser and the extraction of the information.

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• disambiguation of the syntactical category of the words (Part of Speech Tagging);

• partial syntactical analysis (called chunking), aimed at identifying the minimum syntactical groups present in the text in input and at grouping them in constituents;

• semantic analysis and identification of the relevant conceptual structures in the text in input;

• conversion of the analysed document from the original format (Microsoft Word, HTML, RTF, txt, etc.) into the XML format, according to the established DTD.

Technical Tool for the Implementation of the Parser and for Information Extraction (2)


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The software Sophia uses the methodology applied to finite state automata and the finite state transducer.

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Architecture

Tokenization and

morphology

Semantic analysis

Syntactical analysis

XSL

DocsTagger XML

DB


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francesco

This slide showe the work flow of sophia. At the first step we can see the dcocument in input. The input format may be txt, word format, hiper text markup language.At the second phase the parser provide to the normalisation of the entry text, tagging all those structures and textual segments, that can be recognised on the basis of characters or, in other words, and provide to the disambiguation of the syntactical category of the words.Then start a partial syntactical analysis (called chunking), aimed at identifying the minimum syntactical groups present in the text in input and at grouping them in constituents.Finally there is a semantic analysis and identification of the relevant conceptual structures in the text in input.At last step we have the conversion of the analysed document from the original format (Microsoftâ Word, HTML, RTF, txt, etc.) into the XML format, according to the established DTD or the data entry in a data base.

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Identification and Description of the Models on the Basis of Legal RulesThe legislative instrument has, by definition, a

prescriptive function, and in virtue of this, the request is that the legislative instrument responds to a set of rules

that dominate and, at the same time, stand beside, integrate, and sometimes modify the rules that make up common language and these rules too, can be defined as

legal rules

For the implementation of the grammar that will be utilised by the parser, it is necessary to integrate the

models extracted from the legal rules with the linguistic rules. We call these models “well-formed”.


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francesco

The application of a device for parsing, like Sophia 2.1, requires a set of rules to be written for the identification, in the texts, of linguistic structures which are bearers of the information we wish to extract. We can call this, the compilation of a specific grammar of the domain or of the corpus of the texts to be analysed.The grammar is made up of a set of models defining the linguistic structures; in turn, the models include one or more rules representing a linguistic structure which are subsequently compiled according to the syntax of the parser for the text analysis and the information extraction.

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• it must be conferred on the Government;

• it must contain a term within which the Government has to enact the delegated act;

• it must specify the object of the delegation;

• it must contain the guiding principles and criteria to which the Government has to adhere in the exercise of that delegation.

Legislative delegation. The delegation provision must comply:


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francesco

We have decided to experiment the method described for the automated recognition and extraction of three typical structures of legislative instruments, structures representing:•legislative delegation;•express textual amendment or novella;•express external textual reference.The first structure is the legislative delegation and we can see the legal analysis for constructing the model.


Tags or qualifiers of the elements making up the structure

<ADDRESSEE> The Government of the Republic </ADDRESSEE> <ACTION OF DELEGATION> is delegated to enact, </ACTION OF DELEGATION> <TERM> within eighteen months from the date on which this Law comes into force </TERM>, <DELEGATED/ ACT/S> one or more legislative decrees <\DELEGATED/ ACT/S> <OBJECT DELEGATION> laying down additional provisions of the legislation on privacy and personal data protection, </OBJECT DELEGATION> <GUIDING CRITERIA> complying with the following principles and guiding criteria: a) to specify the way in which personal data used for historical, research and statistical purposes shall be processed, taking into account the principles found in ... </GUIDING CRITERIA> (Law 31 December 1996, No. 676).


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francesco

In this slide we have attempted to mark a well-formed model of legislative delegation.


Definition and structural and semantic classification of the explicit text amending provision

AMENDMENT

Repeal Substitution Integration

Part• supra-part• article• paragraph• letter• number

Part of discourse• sentence• phrase• word

actionobject


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Fo r the amendment we are able to propose a classification based on two of the elements we believed to be particularly important: the action of amending and its object.In particular, on the basis of the action of amending, a distinction can be made among the following: repeal, integration and substitution. As far as the object is concerned, the amendment, instead, operates on either a part (supra-part, article, paragraph, etc.) or on a part of the legislative discourse.


Pattern of the Sophia parser

FINE:vpred+DETX?+

RIFB+[M_ALLCAT]*+RIFE:endpos+PUNCTX?+

([E-SUCCESSIVE-MODIFIC-M]|[COME-MODIFICATO-M])?

+PUNCTX?+(INSERIMENTO:vazione|AGGIUNTA:vazione)+

((DETX+AX)|ARTICOLO_I:endnov|COMMA_I:endnov|LET

ERA_I:endnov|NUMERO_I:endnov)+(PUNCTX)?

+VIRGOLETTE:startnov+

[M_ALLCAT-PLUS-RIF]*+VIRGOLETTE:endnov


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This is a rule of formalisation of these rules in the parser’s syntax.In writing the rules which implemented the amendment model we mainly used three of the eight modules included in Sophia 2.1 workbench: the compounder, the lexical semantics module and the sentence level semantics module.In the compounder module, we defined the nominal syntagms important for the purpose of identifying, within the part, the position in which the amendment will act (the final, the last, at the end, before, etc.).In the second module (LexSem), we, instead, defined the verbal voices, with the various synonyms, corresponding to the various actions of amending (to substitute, to repeal, to insert, to add, etc.). in the semantics module is possible to build the rules that make up the models to be extracted. The pattern of this module will be made up of the previously assigned semantic categories. These semantic categories also interact with lexical, morphological and syntactic categories, just as with previously defined macros within the semantic module.



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In this slide we can see the markup of a amendment. The parser sophia has recognized the structure of amendment and has marked in xml the relevant part of the legal model.


Applications and Future Developments of the Project

• The tagging of the amending provision is indispensable for the compilation of a co-ordinated text.

• The recognition of the delegation provision, is necessary to monitor the moment in which the different delegations attributed to the executive will expire.

• Finally, we believe that we can also apply the methodology we have illustrated here, to projects for the control of the quality of legislation.


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In fact, we have seen, from several parts, renewed interest in legislative-drafting analysis (LDA) whose aim is to evaluate the quality of the legislative text and its effect on the legislative order in force.


Index of the quality of the regional laws of Tuscany

The Working Group of the Regional Council of Tuscany has defined the concept of the quality of laws, starting from the assumption that quality is to be understood, as the relationship between the text of the law and the legislative drafting rules


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the Working Group of the Regional Council of Tuscany has prepared an index of the quality of the regional laws of Tuscany.


Experience of The Working Group of the

Regional Council of Tuscany • The rules under examination were those which, having

a high technical profile, could be directly applied by regional legislative offices.

• The analysis based on the comparison between the application and the failure, to apply the rules within a regional law, was conducted step by step, each one corresponding to a qualitative aspect of the law.

• The Working Group drew attention to the rules-quality factors which were on the whole applied with greatest recurrence in 39 laws.


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However, in the legislation quality evaluation, appear to be indispensable (1)

• tools for the automated recognition of natural language so that the text structures that do not comply with the legislative drafting rules can be identified. These tools are even more necessary for the analysis of extended corpora,

• reliable "metrics" for measuring the errors that are found and the subsequent preparation of these measurement in statistical indexes aimed:

1. at expressing the quality levels;

2. at making comparisons.


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Natural language processing methods, and statistical techniques for checking quality, that are wide-spread in many sectors of production, can constitute the technical-scientific support for successfully introducing quality control of legislative texts.


However, in the legislation quality evaluation, appear to be indispensable (2)

• The second necessity which seems impelling is to involve and co-ordinate centres of excellence in the fields of documentation and legal, linguistic and statistical processing and the control and evaluation of quality;

• The Istituto di Teoria e Tecniche dell'Informazione Giuridica (ITTIG), the Accademia della Crusca, the Public Law and Statistics Departments of Florence University have decided, to collaborate together on national and European research projects in this domain.


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francesco

A rigorous and constant discussion amongst specialists in the humanities and sciences is needed, especially if this relationship is consolidated and adds to a long experience, a great opening to the most advanced developments.

Formal Models for a Legislative Grammar. Explicit Text Amendment Andrea Bolioli, Pietro Mercatali,...

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