Generating implicit labels and training a tagging model using such labels
First Claim
1. A training module implemented on one or more computers comprised of one or more processors and storage, for training a tagging model, the training module comprising:
- logic that receives and stores an explicitly-labeled training set in the storage, the explicitly-labeled training set including explicit labels that have been manually selected;
logic that receives and stores an implicitly-labeled training set in the storage, the implicitly-labeled training set including implicit labels that have been generated by a labeling system; and
logic performed by the one or more processors that trains a tagging model based on the explicitly-labeled training set and the implicitly-labeled training set, and that stores the tagging model in the storage, wherein the logic that trains also maximizes a training objective, wherein the training objective is a function of implicit label information, query information, and state variable information.
2 Assignments
0 Petitions
Accused Products
Abstract
A training module is described for training a conditional random field (CRF) tagging model. The training module trains the tagging model based on an explicitly-labeled training set and an implicitly-labeled training set. The explicitly-labeled training set includes explicit labels that are manually selected via human annotation, while the implicitly-labeled training set includes implicit labels that are generated in an unsupervised manner. In one approach, the training module can train the tagging model by treating the implicit labels as non-binding evidence that has a bearing on values of hidden state sequence variables. In another approach, the training module can treat the implicit labels as binding or hard evidence. A labeling system is also described for providing the implicit labels.
17 Citations
18 Claims
-
1. A training module implemented on one or more computers comprised of one or more processors and storage, for training a tagging model, the training module comprising:
-
logic that receives and stores an explicitly-labeled training set in the storage, the explicitly-labeled training set including explicit labels that have been manually selected; logic that receives and stores an implicitly-labeled training set in the storage, the implicitly-labeled training set including implicit labels that have been generated by a labeling system; and logic performed by the one or more processors that trains a tagging model based on the explicitly-labeled training set and the implicitly-labeled training set, and that stores the tagging model in the storage, wherein the logic that trains also maximizes a training objective, wherein the training objective is a function of implicit label information, query information, and state variable information. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A computer readable storage medium storing computer readable instructions, the computer readable instructions providing a training module that when executed by one or more processing devices performs a process comprising:
-
training a tagging model by optimizing a training objective, wherein the training objective applies a representation of conditional probability that includes, in part, a soft evidence feature function, the soft evidence feature function expressing an influence of implicit labels on hidden state variables. - View Dependent Claims (9, 10)
-
-
11. A method using electronic computing functionality to provide implicit labels for use in training a statistical tagging model, comprising:
-
identifying, using the electronic computing functionality, items selected by users in association with queries submitted by the users; identifying, using the electronic computing functionality, schema information associated with the items; selecting, using the electronic computing functionality, implicit labels associated with the queries, based on the schema information training a tagging model based, at least in part, on a training set that includes the implicit labels; and using the tagging model to associate terms of an input query with labels to transform the input query to a structured query. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
-
Specification