Systems and methods for the automatic categorization of text
AssigneeThomson Reuters Enterprise Centre GmbH
InventorsCecil Lee Quartey, Isaac Kriegman
Life of this patent
Estimating the term…
Abstract
Computer implemented methods for categorizing documents are provided that include: receiving a document having a plurality of headnotes and metadata associated with the document, wherein the plurality of headnotes each comprise a segment of text that summarizes at least a portion of the document; predicting using at least a first machine learning model, for at least a first of the plurality of headnotes, a statute pertaining to the first headnote, wherein the predicted statute has associated therewith a taxonomy of topics; predicting using the first machine learning model, a topic from the taxonomy of topics associated with the statute that the first headnote pertains; and associating the first headnote with the predicted topic.
Claims20
A computer implemented method for categorizing documents comprising:
- receiving, by a server computer, a document having a plurality of headnotes and metadata associated with the document, wherein the plurality of headnotes each comprise a segment of text that summarizes at least a portion of the document;
- predicting, by the server computer, using at least a first machine learning model, for at least a first of the plurality of headnotes, a statute pertaining to the first headnote, wherein the predicted statute has associated therewith a taxonomy of topics;
- predicting, by the server computer, using the first machine learning model, a topic from the taxonomy of topics associated with the statute that the first headnote pertains;
- and
- associating, by the server computer, the first headnote with the predicted topic.
A computer implemented method for categorizing documents comprising:
- receiving, by server computer, a document having a plurality of headnotes and metadata associated with the document, wherein the plurality of headnotes each comprise a segment of text that summarizes at least a portion of the document;
- predicting, by the server computer, that at least a first of the plurality of headnote is interpretive of a statute;
- predicting, by the server computer, using at least a first machine learning model, for the first headnotes, a first statute pertaining to the first headnote, wherein the predicted first statute has associated therewith a taxonomy of topics;
- predicting, by the server computer, using the first machine learning model, a topic from the taxonomy of topics associated with the first statute that the first headnote pertains;
- associating, by the server computer, the first headnote with the predicted first statute taxonomy topic;
- predicting, by the server computer, using the first machine learning model, for at least a second of the plurality of headnotes, a second statute pertaining to the second headnote, wherein the predicted second statute has associated therewith a taxonomy of topics;
- predicting, by the server computer, using the first machine learning model, a new topic to be added to the taxonomy associated with the second statute that the second headnote pertains;
- and
- associating, by the server computer, the second headnote with the new predicted second statute taxonomy topic.
Dependencies are inferred from the claim text.