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Semi-supervised Named-Entity Recognition for Product Attribute Extraction in Book Domain

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12504)


Products sold in today’s marketplace are very numerous and varied. One of them is the book product. Detail information about the book, such as the title of the book, author, and publisher, is often presented in unstructured format in the product title. In order to be useful for the commercial applications, for example catalogs, search functions, and recommendation systems, the attributes need to be extracted from the product title. In this study, we apply Named-Entity Recognition model in semi-supervised style to extract the attributes of e-commerce products in book domain. We experiment with the number of features extraction, i.e. lexical, position, word shape, and embedding features. We extract the book attributes from near to 30K product title data with F-1 measure 65%.


  • Book
  • Named-Entity Recognition
  • Attribute extraction
  • Product title
  • E-commerce

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    This number is obtained after conducting empirical observation.


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This research was supported by the research grant from Universitas Indonesia, namely Publikasi Terindeks Internasional (PUTI) Prosiding year 2020 no NKB-854/UN2.RST/HKP.05.00/2020.

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Correspondence to Rahmad Mahendra .

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Putra, H.S., Priatmadji, F.S., Mahendra, R. (2020). Semi-supervised Named-Entity Recognition for Product Attribute Extraction in Book Domain. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham.

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  • Print ISBN: 978-3-030-64451-2

  • Online ISBN: 978-3-030-64452-9

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