Annotation of legal documents with case citations

Abstract

A computer-implemented method includes detecting a first and second set of citations to a plurality of legal cases cited in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining a first and second set of tones corresponding to each citation in the first and second sets of citations, respectively. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones, respectively. The computer-implemented method further includes detecting a first set of annotations corresponding to the first set of citations. The computer-implemented method further includes building a training model, based, at least in part, from at least a subset of the first set of annotations. The computer-implemented method further includes determining a second set of annotations corresponding to the second set of citations.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 detecting a first set of citations to a plurality of legal cases cited in a plurality of legal documents;   determining, based, at least in part, on (i) natural language processing, (ii) text analysis, and (iii) computational linguists, a first set of tones corresponding to each citation in the first set of citations;   determining a first score for each tone in the first set of tones, wherein the first score is determined based, at least in part, on a level of intensity of a tone corresponding to a citation;   detecting a first set of annotations corresponding to the first set of citations, wherein the first set of annotations are case treatments associated with citations to legal cases;   building a training model from the first set of annotations, wherein the training model is built based, at least in part, on:
 mapping each annotation in the first subset based, at least in part, on (i) a tone, and (ii) a score associated with an annotation; 
   detecting a first citation to a first legal case cited in a first legal document, wherein:
 the first legal document is not part of the plurality of legal documents; and 
 the first citation is devoid of any annotations; 
   determining, based, at least in part, on (i) natural language processing, (ii) text analysis, and (iii) computational linguists, a second set of tones corresponding to the first citation;   determining a second score for each tone in the second set of tones, wherein the second score of each tone in second set of tones is determined based, at least in part, on the level of intensity of a tone corresponding to the first citation; and   determining a first type of case treatment for the first citation cited in the first legal document of, at least in part, on analyzing the training model to devise a pattern in the data, wherein:
 the first citation is associated with the first type of case treatment based, at least in part, on (i) a type of tone corresponding to the first citation and (ii) the type of tone having a score that exceeds a given threshold level; and 
 and 
   generating, based on determining the first type of case treatment, a hover box on a computing device, wherein the hover box is (i) functionally related to the first citation of the first legal document and (ii) includes the first type of case treatment associated with the first citation.