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European Conference on Advances in Databases and Information Systems

ADBIS 2018: Advances in Databases and Information Systems pp 173–181Cite as

Integrating Approximate String Matching with Phonetic String Similarity

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11019)

Abstract

Well-defined dictionaries of tagged entities are used in many tasks to identify entities where the scope is limited and there is no need to use machine learning. One common solution is to encode the input dictionary into Trie trees to find matches on an input text. However, the size of the dictionary and the presence of spelling errors on the input tokens have a negative influence on such solutions. We present an approach that transforms the dictionary and each input token into a compact well-known phonetic representation. The resulting dictionary is encoded in a Trie that is about 72% smaller than a non-phonetic Trie. We perform inexact matching over this representation to filter a set of initial results. Lastly, we apply a second similarity measure to filter the best result to annotate a given entity. The experiments showed that it achieved good F1 results. The solution was developed as an entity recognition plug-in for GATE, a well-known information extraction framework.

Keywords

  • Entity recognition
  • Metaphone
  • Text tagging
  • Trie
  • Active nodes
  • Fast similarity search

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Notes

  1. 1.

    http://gitlab.c3sl.ufpr.br/faes/asm/tree/master.

  2. 2.

    https://gitlab.c3sl.ufpr.br/faes/asm/tree/master.

  3. 3.

    https://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings.

  4. 4.

    Implementation from the commons-codec-1.10.jar library, available at https://commons.apache.org/proper/commons-codec/download_codec.cgi.

  5. 5.

    Implementation from lucene-suggest-5.2.1.jar, at http://lucene.apache.org/.

References

  1. Cunningham, H.: Information extraction, automatic. In: Encyclopedia of Language and Linguistics, 2nd edn. (2005)

    Google Scholar 

  2. Cunningham, H., Tablan, V., Roberts, A., Bontcheva, K.: Getting more out of biomedical documents with gate’s full lifecycle open source text analytics. PLOS Comput. Biol. 9(2), e1002854 (2013)

    CrossRef  Google Scholar 

  3. Deng, D., Li, G., Wen, H., Jagadish, H.V., Feng, J.: Meta: an efficient matching-based method for error-tolerant autocompletion. Proc. VLDB Endow. 9(10), 828–839 (2016)

    CrossRef  Google Scholar 

  4. Grishman, R., Sundheim, B.: Message understanding conference-6: a brief history. In: COLING, vol. 96, pp. 466–471 (1996)

    Google Scholar 

  5. Ji, S., Li, G., Li, C., Feng, J.: Efficient interactive fuzzy keyword search. In: Proceedings of the 18th WWW, WWW 2009, Madrid, Spain, pp. 371–380. ACM (2009)

    Google Scholar 

  6. Lamontagne, L., Abi-Zeid, I.: Combining multiple similarity metrics using a multicriteria approach. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 415–428. Springer, Heidelberg (2006). https://doi.org/10.1007/11805816_31

    CrossRef  Google Scholar 

  7. Li, G., Ji, S., Li, C., Feng, J.: Efficient type-ahead search on relational data: a tastier approach. In: Proceedings of the 2009 ACM SIGMOD, SIGMOD 2009, Providence, Rhode Island, USA, pp. 695–706. ACM (2009)

    Google Scholar 

  8. Li, G., Ji, S., Li, C., Feng, J.: Efficient fuzzy full-text type-ahead search. VLDB J. 20(4), 617–640 (2011)

    CrossRef  Google Scholar 

  9. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    CrossRef  Google Scholar 

  10. Navarro, G.: A guided tour to approximate string matching. ACM Comput. Surv. 33(1), 31–88 (2001)

    CrossRef  Google Scholar 

  11. Philips, L.: Hanging on the metaphone. Comput. Lang. Mag. 7(12), 38–44 (1990)

    Google Scholar 

  12. Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2008)

    CrossRef  Google Scholar 

  13. Tissot, H., Peschl, G., Del Fabro, M.D.: Fast phonetic similarity search over large repositories. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014. LNCS, vol. 8645, pp. 74–81. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10085-2_6

    CrossRef  Google Scholar 

  14. Culotta, A., Kristjansson, T., McCallum, A., Viola, P.: Corrective feedback and persistent learning for information extraction. Artif. Intell. 170(14), 1101–1122 (2006)

    CrossRef  MathSciNet  Google Scholar 

  15. Stonebraker, M., Tao, W., Deng, D.: Approximate string joins with abbreviations. Proc. VLDB Endow. 11(1), 53–65 (2017)

    CrossRef  Google Scholar 

  16. Wimalasuriya, D.C., Dou, D.: Ontology-based information extraction: an introduction and a survey of current approaches. J. Inf. Sci. 36(3), 306–323 (2010)

    CrossRef  Google Scholar 

  17. Xiao, C., Qin, J., Wang, W., Ishikawa, Y., Tsuda, K., Sadakane, K.: Efficient error-tolerant query autocompletion. VLDB Endow. 6(6), 373–384 (2013)

    CrossRef  Google Scholar 

  18. Zobel, J., Dart, P.: Phonetic string matching: lessons from information retrieval. In: The 19th SIGIR, SIGIR 1996, Zurich, Switzerland, pp. 166–172. ACM (1996)

    Google Scholar 

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Acknowledgments

This work was partially funded by Project Sistema de Monitoramento de Políticas de Promoção da Igualdade Racial (SNPPIR).

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Authors and Affiliations

  1. C3SL Labs, Universidade Federal do Paraná, Curitiba, Brazil

    Junior Ferri, Hegler Tissot & Marcos Didonet Del Fabro

Authors
  1. Junior Ferri
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  2. Hegler Tissot
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  3. Marcos Didonet Del Fabro
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Corresponding author

Correspondence to Marcos Didonet Del Fabro .

Editor information

Editors and Affiliations

  1. Eötvös Loránd University, Budapest, Hungary

    Prof. András Benczúr

  2. Christian-Albrechts-Universität, Kiel, Germany

    Prof. Dr. Bernhard Thalheim

  3. Eötvös Loránd University, Budapest, Hungary

    Tomáš Horváth

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Ferri, J., Tissot, H., Del Fabro, M.D. (2018). Integrating Approximate String Matching with Phonetic String Similarity. In: Benczúr, A., Thalheim, B., Horváth, T. (eds) Advances in Databases and Information Systems. ADBIS 2018. Lecture Notes in Computer Science(), vol 11019. Springer, Cham. https://doi.org/10.1007/978-3-319-98398-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-98398-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98397-4

  • Online ISBN: 978-3-319-98398-1

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