Scenario Analytics is a type of analysis that focuses on the evaluation of different scenarios, their merits and their consequences. In the context of the legal domain, this could be in the form of analyzing large databases of legal cases, their facts and their claims, to answer questions such as: Do the current facts warrant litigation?, Is the litigation best pursued before a judge or a jury?, How long is it likely to take?, and What are the best strategies to use for achieving the most favorable outcome for the client? In this work, we report on research directed at answering such questions. We use one of a set of jury verdicts databases totaling nearly a half-million records. At the same time, we conduct a series of experiments that answer key questions and build, sequentially, a powerful data-driven legal decision support system, one that can assist an attorney to differentiate more effective from less effective legal principles and strategies. Ultimately, it represents a productivity tool that can help a litigation attorney make the most prudent decisions for his or her client.

Clustering is a useful tool for helping users navigate, summarize, and organize large quantities of textual documents available on the Internet, in news sources, and in digital libraries. A variety of clustering methods have also been applied to the legal domain, with various degrees of success. Some unique characteristics of legal content as well as the nature of the legal domain present a number of challenges. For example, legal documents are often multi-topical, contain carefully crafted, professional, domain-specific language, and possess a broad and unevenly distributed coverage of legal issues. Moreover, unlike widely accessible documents on the Internet, where search and categorization services are generally free, the legal profession is still largely a fee-for-service field that makes the quality (e.g., in terms of both recall and precision) a key differentiator of provided services. This paper introduces a classification-based recursive soft clustering algorithm with built-in topic segmentation. The algorithm leverages existing legal document metadata such as topical classifications, document citations, and click stream data from user behavior databases, into a comprehensive clustering framework. Techniques associated with the algorithm have been applied successfully to very large databases of legal documents, which include judicial opinions, statutes, regulations, administrative materials and analytical documents. Extensive evaluations were conducted to determine the efficiency and effectiveness of the proposed algorithm. Subsequent evaluations conducted by legal domain experts have demonstrated that the quality of the resulting clusters based upon this algorithm is similar to those created by domain experts.

We apply language modeling keyword search augmented with Berger and Lafferty's (1999) translation model for query expansion to formulate three query expansion methods using word co-occurrence statistics from a large external corpus and user clickthrough data. We study the performance of these methods on a vertical domain (case law documents) using standard metrics and an evaluation framework designed specifically to measure the performance of query expansion under varying degrees of query-document term mismatch.

Medical terms occur across a wide variety of legal, medical, and news corpora. Documents containing these terms are of particular interest to legal professionals operating in such fields as medical malpractice, personal injury, and product liability. This paper describes a novel method of tagging medical terms in legal, medical, and news text that is very fast and also has high recall and precision. To date, most research in medical term spotting has been confined to medical text and has approached the problem by extracting noun phrases from sentences and mapping them to a list of medical concepts via a fuzzy lookup. The medical term tagging described in this paper relies on a fast finite state machine that finds within sentences the longest contiguous sets of words associated with medical terms in a medical term authority file, converts word sets into medical term hash keys, and looks up medical concept ids associated with the hash keys. Additionally our system relies on a probabilistic term classifier that uses local context to disambiguate terms being used in a medical sense from terms being used in a non-medical sense. Our method is two orders of magnitude faster than an approach based on noun phrase extraction and has better precision and recall for terms pertaining to injuries, diseases, drugs, medical procedures, and medical devices. The methods presented here have been implemented and are the core engines for a Thomson West product called the Medical Litigator. Thus far, the Medical Litigator has processed over 100 million documents and generated over 165 million tags representing approximately 164,000 unique medical concepts. The resulting system is very fast and posted a recall from 0.79 to 0.93 and precision between 0.94 and 0.97, depending on the document type.

The effectiveness of information retrieval (IR) systems is influenced by the degree of term overlap between user queries and relevant documents. Query-document term mismatch, whether partial or total, is a fact that must be dealt with by IR systems. Query Expansion (QE) is one method for dealing with term mismatch. IR systems implementing query expansion are typically evaluated by executing each query twice, with and without query expansion, and then comparing the two result sets. While this measures an overall change in performance, it does not directly measure the effectiveness of IR systems in overcoming the inherent issue of term mismatch between the query and relevant documents, nor does it provide any insight into how such systems would behave in the presence of query-document term mismatch. In this paper, we propose a new approach for evaluating query expansion techniques. The proposed approach is attractive because it provides an estimate of system performance under varying degrees of query-document term mismatch, it makes use of readily available test collections, and it does not require any additional relevance judgments or any form of manual processing.

Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews and administrative materials. Today, by contrast, there exists enormous growth in lawyers' electronic work product within these environments, specifically within law firms. Along with this growth has come the need for accelerated knowledge management---automated assistance in organizing, analyzing, retrieving and presenting this content in a useful and distributed manner.In cases where a relevant legal taxonomy is available, together with representative labeled data, automated text classification tools can be applied. In the absence of these resources, document clustering offers an alternative approach to organizing collections, and an adjunct to search.To explore this approach further, we have conducted sets of successively more complex clustering experiments using primary and secondary law documents as well as actual law firm data. Tests were run to determine the efficiency and effectiveness of a number of essential clustering functions. After examining the performance of traditional or hard clustering applications, we investigate soft clustering (multiple cluster assignments) as well as hierarchical clustering. We show how these latter clustering approaches are effective, in terms of both internal and external quality measures, and useful to legal researchers. Moreover, such techniques can ultimately assist in the automatic or semi-automatic generation of taxonomies for subsequent use by classification programs.

A major problem facing online information services is how to index and supplement large document collections with respect to a rich set of categories. We focus upon the routing of case law summaries to various secondary law volumes in which they should be cited. Given the large number (> 13,000) of closely related categories, this is a challenging task that is unlikely to succumb to a single algorithmic solution. Our fully implemented and recently deployed system shows that a superior classification engine for this task can be constructed from a combination of classifiers. The multi-classifier approach helps us leverage all the relevant textual features and meta data, and appears to generalize to related classification tasks.

We describe a system that processes court opinions and retrieves related cases from a citator database, so that new cases can be linked to earlier ones that they impact. The design of the system combines information extraction, information retrieval and machine learning techniques in a novel way. The fully implemented program is capable of performing prior case retrieval at human levels of recall and acceptable levels of precision.

Article Free Access Share on Information extraction from case law and retrieval of prior cases by partial parsing and query generation Authors: Peter Jackson Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Khalid Al-Kofahi Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Chris Kreilick Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Brian Grom Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 60–67https://doi.org/10.1145/288627.288642Online:01 November 1998Publication History 9citation464DownloadsMetricsTotal Citations9Total Downloads464Last 12 Months23Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF