We present the SPIRE system, a hybrid case-based reasoning (CBR) and information retrieval (IR) system. It relies on a small case-base to seed the IR system to 1) select documents that are relevant to a presented problem case, and then (2) highlight within these retrieved documents passages that contain relevant information about specific case features. SPIRE aids not only problem-solving but knowledge acquisition by focusing a text extractor---person or program---on areas of text where needed information is likely to be found. Once extracted, this information can be used to create new cases or data-base objects thus closing the loop in the problem-solving---knowledge-acquisition cycle. 1 Introduction When confronted with a new problem, we frequently try to relate it to one that we have dealt with previously. If we have knowledge of a similar prior problem, we might try to apply the same or a related solution. Similarly, we might want to evaluate a scenario and hypothesize the likeli...

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that locates passages likely to contain information about legally relevant features of cases found in full-text court opinions. SPIRE uses an example base of excerpts from past opinions to form queries, which are run by the INQUERY IR text retrieval engine on individual case opinions. These opinions can be those found by SPIRE in a prior stage of processing, which also employs a hybrid CBR-IR approach to retrieve relevant texts from large document corpora. (This aspect of SPIRE was reported on at ICAIL95.) We present an overview of SPIRE, run through an extended example, and give results comparing SPIRE's with human performance. 1 Introduction There is an enormous amount of legal text available on-line and it is growing every day. While this is a decided benefit for legal research, it also presents a problem of how to search it effectively. In particular, it is no easy task to...

In this paper we discuss a hybrid approach combining CaseBased Reasoning (CBR) and Information Retrieval (IR) for the retrieval of legal documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem ease and retrieves texts of relevant cases from a document corpus dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR anatysis and then using texts associated with certain important classes of cases found in this analysis to “seed” a modified version of INQUERY’s relevance feedback mechanism in order to generate a query. Our approach provides two benefits: it extends the reach of CBR (for retrievat purposes) to much larger corpora, and it enables the injection of knowledgebased techniques into traditional IR. We deseribe our CBRIR approach and report on on-going experiments performed in two different legal domains.

We have built a hybrid Case-Based Reasoning (CBR) and Information Retrieval (IR) system that generates a query to the IR system by using information derived from CBR analysis of a problem situation. The query is automatically formed by submitting in text form a set of highly relevant cases, based on a CBR analysis, to a modified version of INQUERY's relevance feedback module. This approach extends the reach of CBR, for retrieval purposes, to much larger corpora and injects knowledge-based techniques into traditional IR. 1 Introduction One strength of Case-Based Reasoning (CBR) systems is the ability to reason about a problem case and perform highly intelligent problem-solving, such as the generation of legal arguments or detailed operational plans [9]. In particular, CBR systems have at their core the ability to retrieve highly relevant cases. However, CBR systems are limited by the availability of cases actually represented in their case bases. Among current case-based reasoning syst...