Extracting diverse attribute-value information from product catalog text via transfer learning

E-commerce sites are increasingly becoming the norm for how consumers search, purchase, and review products. Such sites internally list millions of products, creating a torrent of product options that can overwhelm a browsing consumer. To facilitate their search, it helps to annotate each product with a table of attributes describing general features such as color, size, etc. However, the tables must be provided by the merchant, so there is a business incentive to automate this task by extracting attribute-value information directly from product titles and descriptions. However, while past methods have done extraction for only a handful of attributes, in practice their exists hundreds of diverse attributes. In this thesis, we present a single model for extracting information on all attributes. In addition, we show that incorporating extra information about intra-attribute similarity improves performance for data-poor attributes.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Dirie, Abdi-Hakin A

Advisor dc:contributor.advisor

• Regina Barzilay.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119519

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119519