System And Method For Providing Document Classification Suggestions
A system and method for providing document classification suggestions is provided. Clusters of uncoded documents are obtained. One or more of the uncoded documents in one such cluster are compared to a set of reference documents. Each reference document is assigned with a classification code. Those reference documents that are similar to the one or more uncoded documents are identified. Different types of the classification codes for at least a portion of the similar reference documents are identified and a count of the classification codes assigned to the portion of similar reference documents for each classification code type is obtained. A suggestion for classification of at least one of the one or more uncoded documents is provided based on the count of classification codes for each classification type and one of a presence and absence of each classification code type.
1 . A system for providing document classification suggestions, comprising:
a database to store clusters of uncoded documents; and
a server comprising a central processing unit, memory, an input port to receive the clusters from the database, and an output port, wherein the central processing unit is configured to:
compare one or more of the uncoded documents in one such cluster to a set of reference documents each assigned with a classification code;
identify those reference documents in the set that are similar to the one or more uncoded documents;
identify different types of the classification codes for at least a portion of the similar reference documents and obtaining a count of the classification codes assigned to the portion of similar reference documents for each classification code type; and
provide a suggestion for classification of at least one of the one or more uncoded documents based on the count of classification codes for each classification type and one of a presence and absence of each classification code type.
2 . A system according to claim 1 , further comprising:
a selection module to select the classification type that is present and has a highest count of classification codes as the suggested classification.
3 . A system according to claim 1 , further comprising:
a cluster classification module to classify the cluster of the one or more uncoded documents, comprising:
a counter module to obtain a count of classification codes for each of the classification types based on the similar reference documents and the uncoded documents with assigned classification codes in the cluster; and
an assignment module to assign to the cluster the classification code of the classification type with a highest count of classification codes in the cluster.
4 . A system according to claim 1 , further comprising:
a display to provide the clusters along one or more spines based on a similarity of the uncoded documents.
5 . A system according to claim 4 , further comprising:
a determination module to determine the spine on which the cluster is placed; and
a label module to identify a label associated with the identified spine,
wherein the label is considered with the count of classification codes for each classification type and one of a presence and absence of each classification code type for the assignment of the classification code.
6 . A system according to claim 4 , further comprising:
a document identification module to identify the similar reference documents, comprising:
a determination module to determine a measure for the spine on which the cluster is placed;
a comparison module to compare the measure with each of the reference documents; and
a selection module to select the reference documents most similar to the measure as the similar reference documents.
7 . A system according to claim 1 , further comprising:
a confidence module to determine a confidence level of the suggested classification and to provide the confidence level with the suggested classification.
8 . A system according to claim 1 , further comprising:
a document identification module to identify the similar reference documents, comprising:
a determination module to determine a measure for the one or more uncoded documents in the cluster, wherein the measure comprises one of a center of the cluster, a sample of the uncoded documents, and the cluster center and the sample;
a comparison module to compare the measure with each of the reference documents; and
a selection module to select the reference documents most similar to the measure as the similar reference documents.
9 . A system according to claim 1 , wherein the similar reference documents satisfy a threshold number.
10 . A system according to claim 1 , further comprising:
a receipt module to receive from a user, a location for injecting the similar reference documents into the cluster.
11 . A method for providing document classification suggestions, comprising:
obtaining clusters of uncoded documents;
comparing one or more of the uncoded documents in one such cluster to a set of reference documents each assigned with a classification code;
identifying those reference documents in the set that are similar to the one or more uncoded documents;
identifying different types of the classification codes for at least a portion of the similar reference documents and obtaining a count of the classification codes assigned to the portion of similar reference documents for each classification code type; and
providing a suggestion for classification of at least one of the one or more uncoded documents based on the count of classification codes for each classification type and one of a presence and absence of each classification code type.
12 . A method according to claim 11 , further comprising:
selecting the classification type that is present and has a highest count of classification codes as the suggested classification.
13 . A method according to claim 11 , further comprising:
classifying the cluster with the one or more uncoded documents, comprising:
obtaining a count of classification codes for each of the classification types based on the similar reference documents and the uncoded documents with assigned classification codes in the cluster; and
assigning to the cluster the classification code of the classification type with a highest count of classification codes in the cluster.
14 . A method according to claim 11 , further comprising:
displaying the clusters along one or more spines based on a similarity of the uncoded documents.
15 . A method according to claim 14 , further comprising:
determining the spine on which the cluster is placed; and
identifying a label associated with the identified spine,
wherein the label is considered with the count of classification codes for each classification type and one of a presence and absence of each classification code type for the assignment of the classification code.
16 . A method according to claim 14 , further comprising:
identifying the similar reference documents, comprising:
determining a measure for the spine on which the cluster is placed;
comparing the measure with each of the reference documents; and
selecting the reference documents most similar to the measure as the similar reference documents.
17 . A method according to claim 11 , further comprising:
determining a confidence level of the suggested classification; and
providing the confidence level with the suggested classification.
18 . A method according to claim 11 , further comprising:
identifying the similar reference documents, comprising:
determining a measure for the one or more uncoded documents in the cluster, wherein the measure comprises one of a center of the cluster, a sample of the uncoded documents, and the cluster center and the sample;
comparing the measure with each of the reference documents; and
selecting the reference documents most similar to the measure as the similar reference documents.
19 . A method according to claim 11 , wherein the similar reference documents satisfy a threshold number.
20 . A method according to claim 11 , further comprising:
receiving from a user, a location for injecting the similar reference documents into the cluster.