US 2016/0078364 A1Published application

Computer-Implemented Identification of Related Items

AssigneeMicrosoft Corporation

InventorsYu-Hsiang Chiu, Xin Yu, Arun K. Sacheti

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Abstract

A computer-implemented training system is described herein for generating at least one model component based on labeled training data. The training system produces the labels in the training data by leveraging textual information expressed in already-evaluated documents. In another implementation, the training system generates a first model component and a second model component. In one implementation, in an application phase, a computer-implemented model-application system applies the first model component to identify an initial set of related items that are related to an input item (such as a query). The model-application system then applies the second model component to select a subset of related items from among the initial set of related items.

Claims20

Dependent claims

A computer-implemented method for generating and applying at least one model component, comprising:

in a training system that includes one or more computing devices:
providing at least one seed item;
identifying, for each seed item, a set of candidate items;
using a computer-implemented label-generating component to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information,
the label being generated, using the label-generating component, by:
identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item;
determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information;
and
generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information;
using a computer-implemented feature-generating component to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information;
using a computer-implemented model-generating component to generate and store a model component based on the label information and the feature information;
and
in a model-application system that includes one or more computing devices:
receiving an input item;
applying the model component to generate a set of zero, one, or more related items that are determined, by the model component, to be related to the input item;
generating an output result based at least on the set of related items;
and
providing the output result to an end user,
the model-application system leveraging use of the model component to facilitate efficient generation of the output result.

16 more elements

A computer readable storage medium for storing computer readable instructions, the computer readable instructions implementing a training system when executed by one or more processing devices, the computer readable instructions comprising:

logic configured to identify, for each of a set of seed items, a set of candidate items;
logic configured to generate a label, for each pairing of a particular seed item and a particular candidate item, based on:
evaluation measures which measure an extent to which documents in a set of documents have been assessed as being relevant to the particular seed item;
and
retrieval information which reflects an extent to which the particular candidate item is found in the set of documents;
logic configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item,
said logic configured to generate a label collectively providing label information, when applied to all pairings of seed items and candidate items,
said logic configured to generate a set of feature values collectively providing feature information, when applied to all pairings of seed items and candidate items;
and
logic configured to generate a model component based on the label information and the feature information,
the model component, when applied by a model-application system, identifying, zero, one, or more related items with respect to an input item,
each particular candidate item corresponding to a particular individual candidate item that includes a single linguistic item, or a particular group candidate item that includes a combination of individual candidate items.

9 more elements

One or more computing devices for implementing at least a training system, comprising:

a candidate-generating component configured to generate a set of candidate items for each seed item, for a plurality of seed items;
a label-generating component configured to generate a label for each pairing of a particular seed item and a particular candidate item, to collectively provide label information,
said label being generated, using the label-generating component, by:
identifying a set of documents that have established respective evaluation measures, each evaluation measure reflecting an assessed relevance between a particular document in the set of documents and the particular seed item;
determining whether the particular candidate item is found in each document in the set of documents, to provide retrieval information;
and
generating the label for the particular candidate item based on the evaluation measures associated with the documents in the set of documents and the retrieval information;
a feature-generating component configured to generate a set of feature values for each said pairing of a particular seed item and a particular candidate item, to collectively provide feature information;
and
a model-training component configured to generate and store a model component based on the label information and the feature information.

7 more elements

Dependencies are inferred from the claim text.