公开(公告)号：US10977573B1

公开(公告)日：2021-04-13

申请号：US15130089

申请日：2016-04-15

Applicant: Google Inc.

Inventor： Jeffrey Dalton ,  Karthik Raman ,  Evgeniy Gabrilovich ,  Kevin Patrick Murphy ,  Wei Zhang

IPC: G06N20/00 ,  G06F16/80 ,  G06F40/169

Abstract: Systems and methods provide distantly supervised wrapper induction for semi-structured documents, including automatically generating and annotating training documents for the wrapper. Training of the wrapper may occur in two phases using the training documents. An example method includes identifying a training set of semi-structured web pages having a subject entity that exists in a knowledge base and, for each training page, identifying target objects, identifying predicates in the knowledge base that connect the subject entity to a target objects identified in the training page, and annotating the training page. Annotating a training page includes generating a feature set for a mention of the target object, generating predicate-target object pairs for the mention, and labeling each predicate-target object pair with a corresponding example type and weight. The annotated training pages are used to train the wrapper to extract new subject entities and new facts from the set of semi-structured web pages.

公开(公告)号：US20180189673A1

公开(公告)日：2018-07-05

申请号：US15394875

申请日：2016-12-30

Applicant: Google Inc.

Inventor： Jeffrey Dalton ,  Karthik Raman ,  Tobias Schnabel ,  Evgeniy Gabrilovich

IPC: G06N99/00 ,  G06N3/08 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F16/248 ,  G06F16/951 ,  G06F16/9535 ,  G06N3/08

Abstract: The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.