System and method for determining semantically related terms using an active learning framework
AssigneeYahoo! Inc.
InventorsPradhuman Jhala, Xiaofei He
Life of this patent
Estimating the term…
Abstract
Systems and methods for determining semantically related terms using an active learning framework such as Transductive Experimental Design are disclosed. Generally, to enhance a keyword suggestion tool, an active learning module trains a model to predict whether a term is relevant to a user. The model is then used to present the user with terms that have been determined to be relevant based on the model so that an online advertisement service provider may more efficiently provide a user with terms that are semantically related to a seed set.
Claims19
A computer-implemented method for determining semantically related terms, the method comprising:
- presenting, with a processor, one or more terms of a first plurality of semantically related terms to a user based on a predictive error of each term of the first plurality of semantically related terms;
- receiving, with a processor, an indication of relevance of at least one of the terms of the first plurality of semantically related terms presented to the user;
- training a model, with a processor, to predict an indication of relevance of a term by the user based on the received indication of relevance of at least one of the terms of the first plurality of semantically related terms presented to the user;
- receiving a second plurality of semantically related terms from a keyword suggestion tool;
- and
- presenting, with a processor, one or more terms of the second plurality of semantically related terms to the user based on the model and one or more properties of each term of the second plurality of semantically related terms.
A computer-readable storage medium comprising a set of instructions for determining semantically related terms, the set of instructions to direct a processor to perform acts of:
- presenting one or more terms of a first plurality of semantically related terms to a user based on a predictive error of each term of the first plurality of semantically related terms;
- receiving an indication of relevance of at least one of the one or more terms presented to the user from the first plurality of semantically related terms;
- training a model to predict an indication of relevance of a term by the user based on the received indication of relevance of at least one of the one or more terms presented to the user from the first plurality of semantically related terms;
- receiving a second plurality of semantically related terms from a keyword suggestion tool;
- and
- presenting one or more terms of the second plurality of semantically related terms to the user based on the model and one or more properties of each term of the second plurality of semantically related terms.
A system for determining a semantically related term comprising:
- a keyword suggestion tool comprising a processor and a storage medium, the keyword suggestion tool configured to determine a plurality of semantically related terms based on a seed set;
- an active learning module comprising a processor and a storage medium that is in communication with the keyword suggestion tool, the active learning module configured to:
- receive a first plurality of semantically related terms from the keyword suggestion tool;
- select a first subset comprising at least one term of the first plurality of semantically related terms based on a predictive error of each term of the semantically related terms;
- receive an indication of relevance from a user of at least one term of the first subset;
- train a model to predict an indication of relevance of a term by the user based on the received indication of relevance of the at least one term of the first subset;
- receive a second plurality of semantically related terms from the keyword suggestion tool;
- and
- select a second subset comprising at least one term of the second plurality of semantically related terms based on the model and one or more properties of each term of the second plurality of semantically related terms.
Dependencies are inferred from the claim text.