Arabic Intelligent Retrieval System For Legal Database
 AIRS – LD

El Achkar Mona                    RAMMAL Mahmoud                    NABHAN Philipe
Moacja@ul.edu.lb                     mrammal@ul.edu.lb                         pnabhan@ul.edu.lb

Legal Informatics Center
Lebanese University
Tabara bldg – Sami Solh st.
P.O.B 116/5396
Beirut - Lebanon
Abstract
Although we admit that high precision information retrieval is an extremely hard problem, we believe that we had already achieved a breakthrough. The most helpful element is that we do completely consider the particularities of legal language in one hand, and that we rely on human analysis of each document based on predefined linguistic tools and language patterns on the other hand. This means we achieved normalizing and homogenizing of vocabulary as well as precising the analyze degree and enhancing the human performance. 
The object we will consider in this paper is AIRS- LD “Arabic information retrieval system for legal jurisprudential data-base” we are developing at the legal informatics center at the Lebanese University. This system is intended to overcome the problems arising from the applications’ qualitative requirements in legal information retrieval such as: very user-friendly, reliable and sufficiently intelligent to meet the aims of the users. Our data-base gathers laws, decrees, doctrine and jurisprudentially documents. Therefore our system is supposed to deal with the main legal text problem which derives from the fact  that this text like all other texts written in natural language is not designed to be used by computers.
Our work rely heavily on ontology defined as an “explicit specification of a conceptualization: the objects concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them”

1 - Introduction
IR is difficult because it must overcomes the ambiguities inherent in a language. Indexing specific Keywords (like we used to use and still have use on some data bases) have revealed to be producing poor results because individual terms don’t always line up with concepts. In Keywords searching the problem of information retrieval via keywords centers around two issues: synonymy and homonymy as well as the poor representation of concepts and discourse.  
Thus we had to build tools that help filter  out extraneous information and return only relevant ones. Such level of precision is very hard to accomplish unless systems could interpret or understand meanings of used words in queries, and unless they allow the use of  many words to precise the information looked for. 
2 - Aims of the system
Considering the following facts:
- Reluctance of users to use codified language 
-The limitation of the Boolean search in inverted files with distance operators which deals with the syntactical representation of terms without regard to the semantic meaning.
- Ambiguities generated by syntactic representation search. 
- Imprecision of results search due to single words indexing. 
- High structure of legal text. 
We decided to create an integrated legal information systems. This term stands for a system that can make available to those working in the field of law, whether legislator or law professional, not only the legislation but also the other sources of law they require for carrying on their work, case law and in special cases, legal authorities as well.
In this context the system's aim is twofold: first to build an efficient communication oriented towards jurist, and second, to help enhance information retrieval by boosting both recall and precision at the same time; Thus to improve relation with our Data bases and the documents' relevancy simultaneously, through a familiar user interface.
The followings are the characteristics retrieval functions of the proposed system:
-	An Arabic friendly-user interface.
-	Language oriented indexing and query processing techniques.
-	Enhanced phrases processing based on linguistic processing.
-	Acceptance of semi-natural language sentences as query.
3 - Structure of the system
The basis of the system is a set of synonyms which contains all the terms which can represent a concept and an analysis of the position of words in relation to other textual elements. 
The system is composed of many elements:
- A corpus of Analysis Patterns built upon concepts and relations among them. These concepts are built at the first level through semantic relations between terms.Patterns are a net of relations among concepts. They  allow an in-depth text analysis during indexing as well as for sophisticated query enhancement and result preparations to supply high-quality information retrieval.
- A lexicon of terms. 
- A lexicon of pre-defined concepts extracted of the main concepts present in the original text (which is kept in the data-base as an image linked to the abstract), and represented by the second one (the abstract)
- A thesaurus of concepts.
- An information extraction system.
- An information retrieval system.
4 - Processing legal language
We are dealing with a special language which is the legal one. According to J.Wroblesweski, legal language comes from natural language and adds to it specialized words and specific meanings corresponding to the legal nature of that discourse.the difference between natural language and legal language is semantic not syntactic. It depends on the words as well as their on  specific meanings..
 So not only should the system take into consideration both the textual aspects (morphology, syntax) but also the specific discursivity of the material as well as the users' expertise, be it the judge, the lawyer, the legislator, the administrative or any other searcher.
Also, should be taken into consideration the symbolic meaning in words and the language complexity due to its style.
Making computers understand the natural language in which legal documents and queries are written requires a very in-depth linguistic processing that took into consideration this reality. Thus, we developed many linguistics tools to help represent the meanings of words as well as of concepts, so we can achieve disambiguation of word sense ,expansions as well as extracting implicit information and describing text discourse.
Most of the natural language processing steps performed were done within the framework of the PALL system.
PALL allowed us to disambiguate words based on their relation to other words and on their semantical relations.
It helps also the building of the special lexicon and thesaurus: 
1- to disambiguate meanings so that words like (BIAA)= (environnement) are distinguished in the meaning of (BIAA MOUNASIBA & BIAA KANOUNIA) & (TALAOUTH AL BIAA).
2- to achieve morphological analysis so that roots of the words are presented as tokens rather than words themselves. For example, (NAKEL & NAKILOUN)= (transporteur, transporteurs)  are presented as token (NAKEL)= (transporteur). 
3- to use thesaurus expansion in order to make words like (OUSSOUL 
MOUHAKAMAT)= (procedures civiles) expand to include (TOUROK MOURAJAAT)= (voies de recours)
5 - Structuring The input document
Precision of the information retrieval system is achieved through the architecture and the structure of the input document we build. Our system focuses on the input document structure build upon the analyzed document and representing the search levels: the phrase, the paragraph and the whole abstract.
The architecture is defined through the representation of solutions given to problems and cases, structure is done in the light of the words' disambiguation principles, the sentences’ adequate environment that represent the discourse, and their semantical relations and role in the document.
 The architecture helps creating nets out of legal and factual concepts  by linking them in a way that indicates how and why the case was resolved in a particular meaning which covers the aspect of legal reasoning.
Architecture focuses on the line of reasoning of which the document constitutes the conclusion.
The meaning of concepts is driven through their rank and their closeness to each other.
Taking into consideration the original legal text structure, the legal language characteristics and the legal reasoning, structure of the input document convey exactly the content of the original 
One fully and efficiently.
At the first level, material is analyzed and classified into concepts or themes for the ease of retrieval.
Themes and concepts are represented by “phrases”. Phrases are the basic units of constructing. They describe the explicit and implicit meaning of the original document.
Their relations and order in the abstract describe not only the argumentical reasoning of the judge but also the implicit meaning that makes logical relation between phrases themselves and the reasoning’s environment which make the particularity of each concept, discourse and document.
6 - Search features
The AIRS-LD uses the techniques of full text search. it offers a number of advanced features eliminating some of the Arabic words syntactical problems. It incorporates several of text analysis functions to provide facilities, in addition to the ability to index and search using Arabic legal language, use supporting thesaurus and process queries with a combination of natural language and Boolean parts as well as phonetic searches. 
The system uses the text structure, which makes it more effective than ones that do not differentiate the role or function each concept plays in the text.
The text structure units being sentences, paragraphs and abstract. They represent the search units that can be selected from a list box.
6.1. Searching at the sentence level
Representation of legal knowledge shows that each document contains many concepts, while each cluster of terms represents concepts. It shows also that some words are constituent parts of larger terms or may have a role relationship with each other. So single words are source of ambiguity and that’s why We designed the sentence as an environment for the word following assumptions that:
a. Phrase is the most adequate frame to disambiguate a word and to give it the meaning intended in the search.
b. Word pairs that appears at the same sentence in the same order as in the query (forward pairs), are more likely to provide highly precision in retrieving information than the ones that appear in the inversed order (backward pairs).
 In order to implement those assumptions we determined sentence as a search level for more effective matching between query terms and words in the documents.
We also used the thesaurus to expand query terms to their hyponyms and synonyms as well as to concepts.
6.2.  Searching at the paragraph level
We designed the location model as an alternative system to the semantical net of relations, and based it on the following assumptions.
a. Presence of any given two concepts (represented by two different phrases) from the query in the same paragraph enhances the relevancy of the document.
b. Discourse information is very helpful in determining relevance of a document.
c. The more concepts are located at the same paragraph, the more relevant the document is.  
6.3. Searching at the abstract level
Despite the fact that the abstract (the input structured document) is well structured defined and composed of paragraphs while these latter are composed of concepts, searching by single words at this level is far from being accurate and effective.

7. Displaying the results
The documents selected from the ranked list of results can be displayed in various units, such as hit passage, paragraph (including the hit passage), abstract (including the hit passage).
The salient sentences or words are highlighted so that the user can skim the text quickly too confirm the context around the salient part. Through this operation, the user can verify the context of the hit passages in the abstract reported which include the hit passages in order to examine the validity and reliability of study.

8. Conclusion
AIRS_LD meets our need to a solution that helps enhancing conditions of accessing the pertinent information and providing us with a high precision accuracy information retrieval. This couldn’t have been achieved without processing natural legal language as we did.
The next step shall be as we hope a machine learning program that use the concepts’ patterns in the manually indexed documents to teach the software what attributes make up desired content. 
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