Abstract: A system and method for binary classification of text units such as sentences, paragraphs and documents as either a rule of law (ROL) or not a rule of law (˜ROL). During a training phase of the system and method of the present invention, an initialized knowledge base and labeled or pre-classified sentences are used to build a trained knowledge base. The trained knowledge base contains an equation, a threshold, and a plurality of statistical values called Z values. When inputting text documents for classification, a Z value is generated for each term or token in the input text. The Z values are input to the equation which calculates a score for each sentence. Each calculated score is then compared to the threshold to classify each sentence as either ROL or ˜ROL.

Abstract: 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).

Abstract: An associative text search and retrieval system uses one or more front end processors to interacting with a network having one or more user terminals connected thereto to allow a user to provide information to the system and receive information from the system. The system also includes storage for a plurality of text documents, and at least one processor, coupled to the front end processors and the document storage. The processor(s) search the text documents according to a search request provided by the user and provide to the front end processor a predetermined number of retrieved documents containing at least one term of the search request. The retrieved documents have higher ranks than documents not provided to the front end processor. The ranks are calculated using a formula that varies according to the square of the frequency in each of the text documents of each of the search terms.

Abstract: A short case name generator transforms the long case name of a lawsuit into a short case name format. The text of the long case name is converted to low-level tokens using dictionaries and heuristic rules. Selected tokens are eliminated and other selected tokens are consolidated into higher level tokens. Each of a sequence of stages receives the output tokens from the preceding stage and produce tokens at a higher level of abstraction. Ultimately, the highest level tokens are produced. Selected high-level tokens are deleted and the surviving tokens are broken down to their component tokens, selected ones of which are also deleted. Next, the surviving tokens are converted back into the text they represent. Editing rules are then applied to that text which results in the short case name format.