Jody J. Daniels

Finding legally relevant passages in case opinions

Thii 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.

A case-based approach to intelligent information retrieval

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.

The synergistic application of CBR to IR

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) and Information Retrieval (IR) for the retrieval of full-text documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem case and retrieves texts of relevant cases from a document collection dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR analysis and then using the texts associated with certain important classes of cases found in this analysis to “seed” a modified version of INQUERYs relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. Our approach provides two benefits: it extends the reach of CBR (for retrieval purposes) to much larger corpora, and it enables the injection of knowledge-based techniques into traditional IR. We describe our CBR-IR approach and report on on-going experiments.

A hybrid CBR-IR approach to legal information retrieval

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.

What You Saw Is What You Want: Using Cases to Seed Information Retrieval

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpts from past case texts. Both are used by SPIRE to automatically generate queries, which are then run by the INQUERY full-text retrieval engine on a large text collection in the case of document retrieval and on individual text documents for passage retrieval.

Listen-Communicate-Show (LCS): spoken language command of agent-based remote information access

Listen-Communicate-Show (LCS) is a new paradigm for human interaction with data sources. We integrate a spoken language understanding system with intelligent mobile agents that mediate between users and information sources. We have built and will demonstrate an application of this approach called LCS-Marine. Using LCS-Marine, tactical personnel can converse with their logistics system to place a supply or information request. The request is passed to a mobile, intelligent agent for execution at the appropriate database. Requestors can also instruct the system to notify them when the status of a request changes or when a request is complete. We have demonstrated this capability in several field exercises with the Marines and are currently developing applications of this technology in new domains.

Integrating IR and CBR to locate relevant texts and passages

The paper presents the SPIRE system, a hybrid case based reasoning (CBR) and information retrieval (IR) system that: (1) from a large text collection, retrieves documents that are relevant to a presented problem case; and (2) highlights within those retrieved documents passages that contain relevant information about specific case features. We present an overview of SPIRE, run through an extended example, and present results comparing SPIREs with human performance. We also compare the results obtained by varying the method by which queries are generated. 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 database objects, thus closing the loop in the problem solving knowledge acquisition process.

The pragmatics of taking a spoken language system out of the laboratory

Lockheed Martins Advanced Technology Laboratories has been designing, developing, testing, and evaluating spoken language understanding systems in several unique operational environments over the past five years. Through these experiences we have encountered numerous challenges in making each system become an integral part of a users operations. In this paper, we discuss these challenges and report how we overcame them with respect to a number of domains.