Paper presented at the Sixth German Workshop on Case-Based Reasoning: Foundations, Systems, and Applications, Rostock, Germany.This paper describes one solution developed to convert textual documents
into formlike representations of cases. The experiences described by cases are textual
descriptions of legal decisions. Indexing vocabulary and assignment theory contributed
in gathering expert knowledge to define attributes and values as well as the required
elements to employ template mining. Most index values are automatically extracted by
the use of template mining. The multi-purpose index Theme is automatically assigned
by reusing cases through an elaboration process. Seed cases are used to indicate values
if the new case is a partial match to one in the case base

Barcia, Ricardo M.

Bueno, Tania

Christiane, Eduardo Mattos

Hoeschl, Hugo C.

Martins, Alejandro

Pacheco, Roberto

Weber, Rosina O.

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Reusing Cases to the Automatic Index Assignment 
from Textual Documents 
 
Rosina Weber  Alejandro Martins  Eduardo Mattos 
Tania Bueno  Hugo Hoeschl  Christiane 
Roberto Pacheco  Ricardo M. Barcia 
 
Federal University of Santa Catarina - Graduate Program in Production Engineering 
(PPGEP/UFSC), Florianópolis, SC - BRAZIL, Phone/Fax: +55 48-331-7010 
rosina@eps.ufsc.br 
 
Abstract. This paper describes one solution developed to convert textual documents 
into formlike representations of cases. The experiences described by cases are textual 
descriptions of legal decisions. Indexing vocabulary and assignment theory contributed 
in gathering expert knowledge to define attributes and values as well as the required 
elements to employ template mining. Most index values are automatically extracted by 
the use of template mining. The multi-purpose index Theme is automatically assigned 
by reusing cases through an elaboration process. Seed cases are used to indicate values 
if the new case is a partial match to one in the case base. 
 
1. Introduction 
Textual descriptions of legal decisions are the knowledge source of jurisprudence 
research. Judicial cases are described with natural language text what represents a 
hard-to-use form, therefore demanding case engineering efforts. The review of the 
research brings forward intelligent applications in the legal domain that use 
complex indexes that demand extensive case engineering such as HYPO (Ashley, 
1990), GREBE (Branting, 1991), CABARET (Rissland & Skalak, 1991) and 
BankXX (Rissland et al, 1993 & 1996). The extensive case engineering 
requirements limit the implementation of these systems as effective commercial 
tools to solve real world problems comprising the universe of such domains. The 
significance of this research is the reduction of case engineering requirements, 
facilitating the development of case-based systems to the legal domain what 
eventually improves the judicial system’s quality and celerity. 
This paper describes one method of the system PRUDENTIA (Weber et. al., 1997a; 
Weber 1997, and Weber et. al., 1997b). PRUDENTIA is a Case-Based Reasoner for 
jurisprudence research. The feasibility of this system depends upon the 
development of a methodology that converts the cases in textual form into a 
formlike representation that is amenable to be handled by the case-based reasoner. 
Following this target, we have developed methods (Weber et. al., 1997) based on 
expert knowledge to automatically assign values to indexes using template mining 
(Lawson et. al., 1996). Template Mining is a Natural Language Programming 
technique that extracts data from texts when the text forms recognizable patterns 
from the target to be extracted or its surroundings. A template carries information 
on what to search in the text and it is triggered to extract the parts indicated. 
The formlike cases in the reasoner represent an interpretation of the experiences 
described in the textual decisions. Multi-purpose indexes refer to sets of indexes 
in which one value indicates the category of the index what raises difficulties to 
their assignment by template mining. Elaboration1 consists of inferring values that 
are not on the surface of cases. Reusing cases that have been assigned semi-
automatically as seed cases to an elaboration process supports the automatic 
assignment of multi-purpose indexes. These seed cases provide the required 
knowledge to find the proper values for the indexes in the new cases. 
2. The system:  PRUDENTIA 
Humans employ analogical reasoning to perform jurisprudence research as the 
research is motivated by a new legal situation that is compared to the past legal 
decisions. Thus, the choice of a CBR system to model such task is not only 
appropriate but obvious since each legal decision describes an experience. 
PRUDENTIA is an interpretive case-based reasoner designed to represent analogical 
reasoning to provide efficient jurisprudence research. These days the main source 
of jurisprudence research consists in text database systems. The Information 
Retrieval literature (Blair & Maron, 1985) points out that these systems are 
limited to a recall of around 25%, that is, only this percentage of relevant cases 
are actually retrieved. Besides, the low precision (the second parameter used for 
evaluation of database systems) forces the user to search among several cases to 
find the relevant ones. These two parameters indicate that the use of these systems 
makes the task time-consuming and imprecise. 
Cases in PRUDENTIA are interpretations of legal decisions that are originally 
written in natural language text. The interpretation of each legal decision is 
performed by a legal professional whose expertise is needful to interpret 
experiences within the legal domain. Cases in this reasoner are modeled with a 
formlike representation, i.e., a set of fields (attributes) properly valued. Experts 
have suggested attributes to represent the interpretation of the legal decisions and 
this is the point when we have been confronted with the strongest encumbrance, 
that is about the impossibility of hand-coding the cases. The automatic indexing 
of multi-purpose legal indexes was still one difficulty left to be solved; we 
describe its solution to this problem in next section. 
3. Multi-purpose Legal Indexes 
The expert's interpretation of the legal decisions resulted in several attributes that 
must be defined in the index vocabulary to ensure efficient retrieval. Defining the 
index vocabulary is one of the indexing task steps. CBR literature (Kolodner, 
1993) suggests that the knowledge engineer envisions the whole collection of 
cases seeking for possible values that might comprise the requirements for a good 
index. To define an index, one should ensure that this index has a value in each 
case. Since the task in our system requires a large data base that comprises several 
different types of cases, accomplishing such guideline becomes unfeasible. There 
are some relevant indexes that must be defined in order to ensure proper retrieval. 
On the other hand, there are a great amount of values that occur in the cases that 
must be represented as indexes in order to ensure an efficient similarity 
assessment that results in the retrieval of the most useful cases. We believe that 
the expert interpretation can indeed represent all cases if we efficiently overcome 
the indexing constraint. The indexing constraint refers to the impossibility to 
value one same index in all cases since they represent experiences that vary in 
nature. Evaluating these values, we have noticed that they can be classified into 
groups according to their nature. For example, there are some specific purposes 
that motivate different petition types. An habeas corpus petition is motivated (in 
Brazilian law) by four different applications, i.e., annulment, canceling, mistrial, 
and abatement. In a criminal appeal, there are other motivations such as the ones 
related to categorizing offenses according to their severity, which might be of 
great relevance within the representation of those types of experiences. However, 
one may find a case describing another type of petition in which the motivation 
(application class) is not so important. Rules assigning different weights could be 
a choice if there weren't so many important index values to be used to index some 
cases. 
The solution we have found is to define multi-purpose indexes. These indexes 
comprise multi-purpose values, that can be classified in different classes. 
Following the previous example, if the index value is the habeas corpus 
motivation, the index value will be for instance canceling and the case 
representation will store the class motivation in another attribute which is not an 
index. As a result, by giving to this index a generic name theme, we assign as 
many values as necessary to represent one case, overcoming the necessity of 
acknowledging all the values to every index in all cases. 
The index theme refers to some secondary aspects or circumstances that interpret 
cases. The complexity of the these indexes stems from the fact that they were 
defined to complete the universe of the attributes in describing the content and 
context of the experiences on the legal decisions. The values for the attribute 
theme that may be present in a legal case are grouped into classes of the same 
nature, namely:  class of tests required (mental health evaluation required, 
evaluation of drug dependency required);  class of application (application for 
annulment, application for canceling, abatement or mistrial);  class of external 
context (traffic accident, strikes, penalty reduction);  severity category (cruelty, 
break into), etc.  
In next section we describe how the reuse of cases can support the automatic 
index assignment of values for the index theme. 
4. Reusing Cases to the Automatic Index Assignment 
One of the advantages of CBR concerns to knowledge representation. It is 
claimed that one does not need to model all the interrelations and details of the 
cases for the system to work. Testing and working with this case base of 138 
cases supported such statement. Some new cases described with an argument of 
insanity, for instance, will cause the retrieval of cases that have issues associated 
to mental health such as a court order for mental health evaluation. It does not 
happen because we have explicitly represented this correspondence. Our system 
does not evaluate the similarity of every argument evaluating the semantics of 
each value. It happens because there are concepts that are inherent to cases where 
the defendant was sent to a health institution that are also present when insanity is 
pleaded. This is a result of dealing with actual experiences. 
At this point, we attempt to make use of this advantage in another fashion. Since 
judicial decisions describe experiences that comprise inherently correlated issues, 
we may comprehend these cases as consistent in nature. Therefore, it is valid to 
attempt to search for values to secondary issues and circumstances in cases that 
are somehow similar to a given new one we are attempting to ascribe. This is why 
we can reuse seed cases, to point what types of values to search in the new case. 
The reuse of cases is motivated by the elaboration method 1. The idea of reusing 
cases to suggest possible new values can be extended to suggest values to new 
cases to be incorporated in the case base.  
The last prototype of the system PRUDENTIA has a case base in which its cases 
were converted semi-automatically. One type of index - theme- was hand-coded. 
This has limited the case base size to only 138 cases. The next step is to increase 
this case base by indexing automatically 3,700 cases, which have all the attributes 
valued except for the index theme. These 138 seed cases have been valued 
                                                 
1 From K. Branting, (1991), case elaboration consists of inferring facts that are 
not explicitly stated in cases in order to improve their match. Case elaboration 
was termed knowledge-based pattern matching in (Porter et al., 1990) In the 
context of analogical reasoning, the process of determining "implicit shared 
properties" of cases from differing explicit representations has been termed 
reformulation (Russel, 1986). A related notion in machine learning is constructive 
induction which has been defined as "any form of induction that generates new 
descriptors not present in the input data"(Dietterich and Michalski, 83). Kolodner 
(1993) describes the elaboration process to support situation assessment;  when 
the value of an index has to be derived, one can perform a partial retrieval and 
search throughout the similar cases for possible values to such features. 
automatically except for the index theme. From a case base of 138 seed cases we 
aim at generating automatically a case base with 3,700 cases within the same sub 
domain (criminal appeals).  
Our problem refers to finding possible values of the attribute theme in 3,700 cases 
that are represented through other attributes. Once we assess the similarity of each 
new case in comparison to the 138 in the complete case base, we reuse the 
knowledge embedded in this case base to derive hints on what types of values to 
search in the new cases. Therefore, we develop a reasoner to perform incremental 
learning to enlarge the original case base. 
The case base of 138 cases embodies an amount of 300 values for attribute theme 
that are being used as seeds in the learning process. These 300 values are 
represented in terms of concepts, objects and context and of their relationships 
with the case which they are derived. 
The input cases are composed by new decisions that have been automatically 
modeled and indexed except for the index theme. When a new case is input, the 
reasoner searches the case base for similar decisions, relying only on the indexes 
that have been valued in the new case. The outcome of the retrieval process is a 
set of similar cases that is presented to the user sorted by a similarity measure, 
indicating the given values for attribute theme. The user (expert) chooses the 
cases and values the system is supposed to attempt to find in the new case. This is 
an iterative process that ends up with a number of suggested values to assign in 
the new case. The user is asked to review these new values before adding this new 
case to the base. 
If a similar value for theme is found, the user (the expert) is asked whether this 
new value can be added to the new case to be learned. As a result, we can reuse 
the knowledge embedded in the cases of the case base and besides, use the 
knowledge present in the new cases. This is possible because the content of the 
legal decisions are somehow interconnected. Hence, instead of trying to represent 
symbolically these interconnections, we infer on it making use of the context of 
the new cases. 
5. Concluding Remarks 
The motivation for the implementation introduced here is another way of making 
use of the well known advantage of case-based reasoners that bypasses explicit 
knowledge representation. The analogical reasoning behind the use of cases 
comprise indeed an effective approach when we can certify the inherent 
correspondence of concepts within case descriptions. 
The definition of multi-purpose indexes is an alternative when one cannot 
comprise a value for an index in every case of the collection. Large case-based 
reasoners can make use of such alternative. 
6. References 
Ashley, Kevin D. (1990). Modeling Legal Argument:  reasoning with cases and 
hypotheticals. A Bradford book. The MIT Press, Cambridge, Massachussetts. 
Blair, D. C. and Maron, M.E. (1985). "An Evaluation of Retrieval Effectiveness 
for a Full-Text Document-Retrieval System". Communications of the ACM, 
28, 3, 280-299. 
Branting, L. Karl (1991). Reasoning with Portions of Precedents, Proceedings of 
the Third International Conference on Artificial Intelligence and Law, 
Oxford, England, June 25--28, 1991. 
Branting, L. Karl (1991b). Building explanations from rules and structured cases. 
International Journal of Man-Machine Studies, 34, 797-837. 
Leake, David. (1996). Case-Based Reasoning:  Experiences, Lessons, & Future 
Directions. AAAI Press/The MIT Press, Menlo Park, California, 1996. 
Rissland, E. L., & Skalak, D. B. (1991). "CABARET: Statutory Interpretation in a 
Hybrid Architecture." International Journal of Man-Machine Studies, June, 
1991. (34):839-887. 
Rissland, E.L., Skalak, D.B., & Friedman. (1993). M.T. "BANKXX: A Program 
to Generate Argument through Case-Based Search." Proceedings of the 
Fourth International Conference on AI and Law (ICAIL-93) , Amsterdam, 
The Netherlands, June 1993. pp. 117-124. 
Rissland, E., Skalak, D. & Friedman, M. (1996) Using Heuristic Search to 
Retrieve Cases that Support Arguments. In Case-Based Reasoning:  
Experiences, Lessons, & Future Directions. David Leake, ed.  AAAI 
Press/The MIT Press. 
Weber-Lee, R.; Barcia, R.; Costa, M.; Rodrigues Filho, I.;  Hoeschl, H. C.; 
Bueno, T.;  Martins, A;  & Pacheco, R. (1997a). A Large Case-Based 
Reasoner for Legal Cases. Lecture Notes in Artificial Intelligence:  2nd Int. 
Conference on CBR, ICCBR97. David Leake, Enric Plaza (ed.)-Berlin: 
Springer. 
Weber, (1997) PRUDENTIA:  Enabling a real world application of Case-Based 
Reasoning to jurisprudence research. Qualifying Examination approved by 
the Graduate Program of Production Engineering at the Federal University of 
Santa Catarina. In Activities. Available online  
http://www.eps.ufsc.br/~rosina/html/activities.html, [October, 1997]. 
Weber, Rosina; Barcia, Ricardo; Pacheco, Roberto; Martins, Alejandro; Hoeschl, 
Hugo; Bueno, Tania; Costa, Marcio; Rodrigues Filho, Ilson. (1997b). 
Representing Cases From Texts In Case-Based Reasoning. III Congresso 
Internacional de Engenharia Industrial e XVII ENEGEP, 6 a 9 de Outubro de 
1997, Canela, RS, Brasil. 


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