Lexisnexis Group

Lexisnexis

Lexisnexis, a Division of Reed Elsevier Inc.

Lexis-nexis Group, a Division of Reed Elsevier Inc.

James S Wiltshire, Jr

Timothy Lee Humphrey

Salahuddin Ahmed

John T Morelock

Xin Allan Lu

Spiro G Collias

Paul Zhang

Afsar Parhizgar

Joseph P Harmon

X Allan Lu

Donald Loritz

Mark Wasson

Shian-jung Chen

Valentina Templar

Steve Xu

Eleni Koutsomitopoulou

James M Peck

A fact extraction tool set ('FEX') finds and extracts targeted pieces of information from text using linguistic and pattern matching technologies, and in particular, text annotation and fact extraction. Text annotation tools break a text, such as a document, into its base tokens and annotate those tokens or patterns of tokens with orthographic, syntactic, semantic, pragmatic and other attributes. A user-defined 'Annotation Configuration' controls which annotation tools are used in a given application. XML is used as the basis for representing the annotated text. A tag uncrossing tool resolves conflicting (crossed) annotation boundaries in an annotated text to produce well-formed XML from the results of the individual annotators. The fact extraction tool is a pattern matching language which is used to write scripts that find and match patterns of attributes that correspond to targeted pieces of information in the text, and extract that information.

System and method for extracting information from text using text annotation and fact extraction

A computer-automated system and method identify text in a first 'citing' court case, near a 'citing instance' (in which a second &#x201c;cited&#x201d; court case is cited), that indicates the reason(s) for citing (RFC). The automated method of designating text, taken from a set of citing documents, as reasons for citing (RFC) that are associated with respective citing instances of a cited document, has steps including: obtaining contexts of the citing instances in the respective citing documents (each context including text that includes the citing instance and text that is near the citing instance), analyzing the content of the contexts, and selecting (from the citing instances' context) text that constitutes the RFC, based on the analyzed content of the contexts. A related computer-automated system and method selects content words that are highly related to the reasons a particular document is cited, and gives them weights that indicate their relative relevance. Another related computer-automated system and method forms lists of morphological forms of words. Still another related computer-automated system and method scores sentences to show their relevance to the reasons a document is cited. Also, another related computer-automated system and method generates lists of content words. In a preferred embodiment, the systems and methods are applied to legal (especially case law) documents and legal (especially case law) citations.

Automated system and method for generating reasons that a court case is cited

A computer-implemented method of gathering large quantities of training data from case law documents (especially suitable for use as input to a learning algorithm that is used in a subsequent process of recognizing and distinguishing fact passages and discussion passages in additional case law documents) has steps of: partitioning text in the documents by headings in the documents, comparing the headings in the documents to fact headings in a fact heading list and to discussion headings in a discussion heading list, filtering from the documents the headings and text that is associated with the headings, and storing (on persistent storage in a manner adapted for input into the learning algorithm) fact training data and discussion training data that are based on the filtered headings and the associated text. Another method (of extracting features that are independent of specific machine learning algorithms needed to accurately classify case law text passages as fact passages or as discussion passages) has steps of: determining a relative position of the text passages in an opinion segment in the case law text, parsing the text passages into text chunks, comparing the text chunks to predetermined feature entities for possible matched feature entities, and associating the relative position and matched feature entities with the text passages for use by one of the learning algorithms. Corresponding apparatus and computer-readable memories are also provided.

System and method for identifying facts and legal discussion in court case law documents

Computer-based system and method for finding rules of law in text

An economic, scalable machine learning system and process perform document (concept) classification with high accuracy using large topic schemes, including large hierarchical topic schemes. One or more highly relevant classification topics is suggested for a-given document (concept) to be classified. The invention includes training and concept classification processes. The invention also provides methods that may be used as part of the training and/or concept classification processes, including: a method of scoring the relevance of features in training concepts, a method of ranking concepts based on relevance score, and a method of voting on topics associated with an input concept. In a preferred embodiment, the invention is applied to the legal (case law) domain, classifying legal concepts (rules of law) according to a proprietary legal topic classification scheme (a hierarchical scheme of areas of law).

System and method for classifying legal concepts using legal topic scheme

Growing community of inventors

Springboro, OH, United States of America

James S Wiltshire, Jr