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

• Main Author: Dirie, Abdi-Hakin A
• Other Authors: Regina Barzilay.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2018
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/119519

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. === This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 63-64). === 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. === by Abdi-Hakin A. Dirie. === M. Eng.