{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119519"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119519","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Extracting diverse attribute-value information from product catalog text via transfer learning","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Dirie, Abdi-Hakin A"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Regina Barzilay."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:21:35Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Extracting diverse attribute-value information from product catalog text via transfer learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Regina Barzilay."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Dirie, Abdi-Hakin A"],"dc:date.accessioned":["2018-12-11T20:38:30Z"],"dc:date.available":["2018-12-11T20:38:30Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. 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