Abstract: A computer-implemented method of semantically linking, by a computing device, a citing document to a cited document in a corpus of documents includes locating at least one citation present in the citing document, determining a reason-for-citing for at least one citation, and determining a cited-text-area present in the cited document, wherein the cited-text-area corresponds with the reason-for-citing. The method further includes populating a citation entry of a citation-pairing metadata file with a citing-document identifier, a reason-for-citing identifier, a cited-document identifier, and a cited-text-area identifier. The citation-pairing metadata file includes a plurality of citation entries and is stored separately from the citing document and the cited document.

Abstract: A computer-implemented method of semantically linking, by a computing device, a citing document to a cited document in a corpus of documents includes locating at least one citation present in the citing document, determining a reason-for-citing for at least one citation, and determining a cited-text-area present in the cited document, wherein the cited-text-area corresponds with the reason-for-citing. The method further includes populating a citation entry of a citation-pairing metadata file with a citing-document identifier, a reason-for-citing identifier, a cited-document identifier, and a cited-text-area identifier. The citation-pairing metadata file includes a plurality of citation entries and is stored separately from the citing document and the cited document.

Abstract: An approach is provided where a request is received to rotate graphical objects displayed on a display screen from a first orientation to a second orientation, the request received from a user operating the machine. The second orientation of the graphical objects is calculated based on a rotational reference that is included in the request. The graphical objects are then displayed on the display screen at the calculated second orientation, with the second orientation being visually rotated from the first orientation.

Abstract: According to one disclosed embodiment, an approach is provided in which a user action directed at a selected graphical user interface (GUI) control displayed on a display screen is received at a machine. The selected GUI control corresponds to a software application and the selected GUI control is used to launch the software application. The system detects that the user action is a billboard request. Billboard data corresponding to the software application is retrieved and displayed on the display screen. In addition, the displaying of the billboard data inhibits execution of the software application.

Abstract: A computer-implemented method of semantically linking, by a computing device, a citing document to a cited document in a corpus of documents includes locating at least one citation present in the citing document, determining a reason-for-citing for at least one citation, and determining a cited-text-area present in the cited document, wherein the cited-text-area corresponds with the reason-for-citing. The method further includes populating a citation entry of a citation-pairing metadata file with a citing-document identifier, a reason-for-citing identifier, a cited-document identifier, and a cited-text-area identifier. The citation-pairing metadata file includes a plurality of citation entries and is stored separately from the citing document and the cited document.

Abstract: A computer-automated system and method identify text in a first “citing” court case, near a “citing instance” (in which a second “cited” 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.

Abstract: A computer-automated system and method identify text in a first “citing” court case, near a “citing instance” (in which a second “cited” 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.

Abstract: A computer-automated system and method identify text in a first “citing” court case, near a “citing instance” (in which a second “cited” 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.

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).