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Data Driven Discovery of Attribute Dictionaries

  • Fei ChiangEmail author
  • Periklis Andritsos
  • Renée J. Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9630)

Abstract

Online product search engines such as Google and Yahoo shopping, rely on having extensive and complete product information to return accurate and timely search results. Given the expanding scope of products and updates to existing products, automated techniques are needed to ensure the underlying product dictionaries remain current and complete. Product search engines receive offers from merchants describing product specific attributes and characteristics. These offers normally contain structured attribute-value pairs, and unstructured (textual) descriptions describing product characteristics and features. For example, a laptop offer may contain attribute-value pairs such as “model-X42” and “RAM-8 GB”, and a text description of the software, accessories, battery features, warranty, etc. Updating the product dictionaries using the textual descriptions is a more challenging task than using the attribute-value pairs since the relevant attribute values must first be extracted. This task becomes difficult since the text descriptions often do not follow a predefined format, and the data in the descriptions vary across different merchants and products. However, this information needs to be captured to ensure a comprehensive and complete product listing. In this paper, we present techniques that extract attribute values from textual product descriptions. We introduce an end-to-end framework that takes an input string record, and parses the tokens in a record to identify candidate attribute values. We then map these values to attributes. We take an information theoretic approach to identify groups of tokens that represent an attribute value. We demonstrate the accuracy and relevance of our approach using a variety of real data sets.

Keywords

Information extraction Clustering Dictionaries 

References

  1. 1.
    Yelp dataset challenge (2011). www.yelp.ca/dataset_challenge
  2. 2.
    General electric second annual major purchase shopper study. GE Capital Retail (2013)Google Scholar
  3. 3.
    Agathangelou, P., Katakis, I., Kokkoras, F., Ntonas, K.: Mining domain-specific dictionaries of opinion words. In: Web Information Systems Engineering, pp. 47–62 (2014)Google Scholar
  4. 4.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD Conference, pp. 207–216 (1993)Google Scholar
  5. 5.
    Bing, L., Lam, W., Wong, T.-L.: Wikipedia entity expansion and attribute extraction from the web using semi-supervised learning. In: International Conference on Web Search and Data Mining, WSDM, pp. 567–576 (2013)Google Scholar
  6. 6.
    Borkar, V., Deshmukh, K., Sarawagi, S.: Automatic segmentation of text into structured records. SIGMOD Rec. 30(2), 175–186 (2001)CrossRefGoogle Scholar
  7. 7.
    Chaturvedi, S., Prasad, K.H., Faruquie, T.A., Chawda, B., Subramaniam, L.V., Krishnapuram, R.: Automating pattern discovery for rule based data standardization systems. In: ICDE, pp. 1231–1241 (2013)Google Scholar
  8. 8.
    Chiang, F., Andritsos, P., Zhu, E., Miller, R.J.: Autodict: automated dictionary discovery. In: ICDE, pp. 1277–1280 (2012)Google Scholar
  9. 9.
    Cohen, W.W., Sarawagi, S.: Exploiting dictionaries in named entity extraction: combining semi-markov extraction processes and data integration methods. In: SIGKDD, pp. 89–98 (2004)Google Scholar
  10. 10.
    Cortez, E., da Silva, A.S., Gonçalves, M.A., de Moura, E.S.: Ondux: on-demand unsupervised learning for information extraction. In: SIGMOD Conference, pp. 807–818 (2010)Google Scholar
  11. 11.
    Cover, T., Thomas, J.: Elements of Information Theory. Wiley, New York (1991)CrossRefzbMATHGoogle Scholar
  12. 12.
    Godbole, S., Bhattacharya, I., Gupta, A., Verma, A.: Building re-usable dictionary repositories for real-world text mining. In: CIKM, pp. 1189–1198 (2010)Google Scholar
  13. 13.
  14. 14.
    Klein, D., Manning, C.: Accurate unlexicalized parsing. In: Proceedings of ACL, pp. 423–430 (2003)Google Scholar
  15. 15.
    Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)Google Scholar
  16. 16.
    Lee, T., Wang, Z., Wang, H., Hwang, S.-W.: Attribute extraction and scoring: a probabilistic approach. In: ICDE, pp. 194–205 (2013)Google Scholar
  17. 17.
    Li, G., Deng, D., Feng, J.: Faerie: efficient filtering algorithms for approximate dictionary-based entity extraction. SIGMOD 2011, pp. 529–540 (2011)Google Scholar
  18. 18.
    Li, X., Wang, Y.-Y., Acero, A.: Extracting structured information from user queries with semi-supervised conditional random fields. In: SIGIR 2009, pp. 572–579 (2009)Google Scholar
  19. 19.
    Nguyen, H., Fuxman, A., Paparizos, S., Freire, J., Agrawal, R.: Synthesizing products for online catalogs. Proc. VLDB Endow. 4(7), 409–418 (2011)CrossRefGoogle Scholar
  20. 20.
    Pantel, P., Philpot, A., Hovy, E.H.: An information theoretic model for database alignment. In: SSDBM, pp. 14–23 (2005)Google Scholar
  21. 21.
    Peshkin, L., Pfeffer, A.: Bayesian information extraction network. In: IJCAI 2003, pp. 421–426 (2003)Google Scholar
  22. 22.
    Rissanen, J.: Modeling shortest data description. In: Automatica (1978)Google Scholar
  23. 23.
    Roy, S., Chiticariu, L., Feldman, V., Reiss, F., Zhu, H.: Provenance-based dictionary refinement in information extraction. In: SIGMOD Conference, pp. 457–468 (2013)Google Scholar
  24. 24.
    Sarawagi, S., Cohen, W.W.: Semi-markov conditional random fields for information extraction. In: NIPS, pp. 1185–1192 (2004)Google Scholar
  25. 25.
    Sarkas, N., Paparizos, S., Tsaparas, P.: Structured annotations of web queries. In: SIGMOD Conference, pp. 771–782 (2010)Google Scholar
  26. 26.
    Slonim, N., Tishby, N.: Agglomerative information bottleneck. In: NIPS, pp. 617–623 (1999)Google Scholar
  27. 27.
    Socher, R., Bauer, J., Manning, C., Ng, A.: Parsing with compositional vector grammars. In: Proceedings of ACL, pp. 455–465 (2013)Google Scholar
  28. 28.
    Sutton, C., Mccallum, A.: Introduction to Conditional Random Fields for Relational Learning. MIT Press, Cambridge (2006)zbMATHGoogle Scholar
  29. 29.
    Tan, B., Peng, F.: Unsupervised query segmentation using generative language models and wikipedia. In: WWW, pp. 347–356 (2008)Google Scholar
  30. 30.
    Winkler, W.E.: String comparator metrics and enhanced decision rules in the fellegi-sunter model of record linkage. In: Survey Research, pp. 354–359 (1990)Google Scholar
  31. 31.
    Zhang, M., Hadjieleftheriou, M., Ooi, B.C., Procopiuc, C.M., Srivastava, D.: Automatic discovery of attributes in relational databases. In: SIGMOD Conference, pp. 109–120 (2011)Google Scholar
  32. 32.
    Zhang, Z., Zhu, K.Q., Wang, H., Li, H.: Automatic extraction of top-k lists from the web. In: ICDE, pp. 1057–1068 (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Fei Chiang
    • 1
    Email author
  • Periklis Andritsos
    • 2
  • Renée J. Miller
    • 3
  1. 1.McMaster UniversityHamiltonCanada
  2. 2.University of LausanneLausanneSwitzerland
  3. 3.University of TorontoTorontoCanada

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