Improving Classification Accuracy Using Automatically Extracted Training Data

Ariel FuxmanA. Kannan, A. Goldberg, R. Agrawal,

P. Tsaparas, J. Shafer

Search LabsMicrosoft Research – Silicon Valley

Mountain View, CA

• For classification tasks, large amounts of training data can significantly improve accuracy

• How do we create large training sets?– Conventional methods of using human labelers

are expensive and do not scale

• Thesis: The Web can be used to automatically create labeled data

Web as a Source of Training Data

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In this talk

• Validate the thesis on a task of practical importance: Retail intent identification in Web Search

• Present desirable properties of sources of labeled data

• Show how to extract labeled data from the sources

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Importance of Retail Intent Queries

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Just Behave: A Look at Searcher Behavior -Total U.S. MarketComScore Feb 2009Just Behave: A Look at Searcher Behavior -Total U.S. MarketComScore Feb 2009

Share of Searches(% of total search queries)

Share of Paid Clicks(% of queries leading to paid clicks)

Application of Retail Intent

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• Provide enhanced user experience around Commerce Search

Retail intent identificationDefinition:

A query posed to a search engine has retail intent if most users who type the query have the intent to buy a tangible product

Examples :Queries with retail intent Queries without retail

intent

Zune 80 gb Medical insurance

Buy ipod Free ringtones

Digital camera lenses Digital camera history

Data Sources for Retail Intent• Sources

– Web sites of retailers (e.g., Amazon, Walmart, Buy.com)

• Training Data– Queries typed directly on search box of retailers

• Extraction from toolbar logs

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URL in toolbar log

Desirable Properties of Web Data Sources

• Popularity– Sources should yield large amounts of data

• Orthogonality– Sources should provide training data about

different regions of the training space

• Separation– Sources should provide either positive or negative

examples of the target class, but not both

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Popularity• Sources should yield large amounts of

data• For retail intent identification

–Web site traffic is a proxy for popularity –More traffic means more queries–Choose Web sites of retailers based on

publicly available traffic report (Hitwise)

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Orthogonality

• For retail intent identification• Positive examples: top sites from “Departmental Stores” and

“Classified Ads” (Amazon and Craigslist)

• Negative examples: top site from “Reference” (Wikipedia)

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• Sources should provide training data about different regions of the training space

Separation

• Training examples must unambiguously reflect the intended meaning of most users

– Example: there is a book called “World War I”, but the intent of the query is mostly non-commercial

• Can be enforced by removing groups of confusable queries from the sources

Method to Enforce Separation

• Create “groups” of positive queries• Compare the word frequency distribution of

each group against the negative class using Jensen-Shannon divergence

• Remove groups with low divergence

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Groups for Retail Intent

• Extracting groups from the toolbar log

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URL in toolbar log

Enforcing separation property• JS Divergence of Amazon and Craigslist with

respect to Wikipedia

See paper for experimental validation

Experiments

• Setup– Built multiple classifiers using manual and

automatically extracted labels in the training sets– Classification method: logistic regression, using

unigrams and bigrams as features– Test set: 5K queries randomly sampled from a

query log and labeled using Mechanical Turk

Automatic vs. Manual

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Accuracy of extracted labels classifier on par with manual labels classifier

Combining Manual and Automatically Extracted

Marginally different from using only automatically extracted labels

Using Unlabeled Data

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Performance of the automatic labels classifier is still on par with classifiers that start with manual labels and exploit unlabeled data using self-training

Conclusions

• By carefully choosing the data sources, we can extract valuable training data

• Using large amounts of automatically extracted training data, we can get classifiers that are on par with those trained with manual labels

• As future work, we would like to apply this experience to other classification tasks

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