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Purpose
Context Administration Classification Training Service
AUTHID
CURRENT_USER
Constants
Name
Data Type
Value
General
SENTIMENT_NUL
NUMBER
0
SENTIMENT_POS
NUMBER
1
SENTIMENT_NEG
NUMBER
2
Data Types
TYPE doc_rec IS RECORD (
docid NUMBER, -- document ID to identify the document
clusterid NUMBER, -- the ID of the cluster the document is assigned to
score NUMBER); -- the similarity score between document and cluster
TYPE doc_tab IS TABLE OF doc_rec INDEX BY BINARY_INTEGER;
/* in-memory table for cluster information */
TYPE cluster_rec IS RECORD (
clusterid NUMBER, -- cluster ID to identify a cluster
descript drvutl.dr_extrabuf, --string to describe the cluster
label drvutl.dr_longbuf, -- a suggested label for the cluster
sze NUMBER, -- number of documents assigned to the cluster
quality_score NUMBER, -- the quality score of the cluster
parent NUMBER); -- parent cluster id. negative means no parent
TYPE cluster_tab IS TABLE OF cluster_rec INDEX BY BINARY_INTEGER;
TYPE docid_tab IS TABLE OF NUMBER INDEX BY BINARY_INTEGER;
Generates a set of sub-group (clusters) from a provided collection of documents
Overload 1
ctx_cls.clustering(
index_name IN VARCHAR2,
docid IN VARCHAR2,
doctab_name IN VARCHAR2,
clstab_name IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(clustering, AUTO);
TBD
Generates a set of sub-group (clusters) from a provided collection of documents
Overload 2
ctx_cls.clustering(
index_name IN VARCHAR2,
docid IN VARCHAR2,
dids IN docid_tab,
doctab_name IN OUT NOCOPY doc_tab,
clstab_name IN OUT NOCOPY cluster_tab,
pref_name IN VARCHAR2 DEFAULT NULL);
Generates sentiment model based on provided training set data
ctx_cls.sa_train_model(
clsfier_name IN VARCHAR2,
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(sa_train_model, AUTO);
Automatically generates ctx-rules from training examples
Overload 1
ctx_cls.train(
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
restab IN VARCHAR2,
rescatid IN VARCHAR2,
resquery IN VARCHAR2,
resconfid IN VARCHAR2,
pref_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(train, AUTO);
TBD
Automatically generates predicative model from examples for classification
Overload 2
ctx_cls.train(
index_name IN VARCHAR2,
docid IN VARCHAR2,
cattab IN VARCHAR2,
catdocid IN VARCHAR2,
catid IN VARCHAR2,
restab IN VARCHAR2,
pref_name IN VARCHAR2);
PRAGMA SUPPLEMENTAL_LOG_DATA(train, AUTO);
