Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling

  • Yiting Ju
  • Benjamin Adams
  • Krzysztof Janowicz
  • Yingjie Hu
  • Bo Yan
  • Grant McKenzie
Conference paper

DOI: 10.1007/978-3-319-49004-5_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)
Cite this paper as:
Ju Y., Adams B., Janowicz K., Hu Y., Yan B., McKenzie G. (2016) Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling. In: Blomqvist E., Ciancarini P., Poggi F., Vitali F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science, vol 10024. Springer, Cham

Abstract

Place name disambiguation is the task of correctly identifying a place from a set of places sharing a common name. It contributes to tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here we propose a novel approach to the disambiguation of place names from short texts that integrates two models: entity co-occurrence and topic modeling. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. We evaluate our approach using a corpus of short texts, determine the suitable weight between models, and demonstrate that a combined model outperforms benchmark systems such as DBpedia Spotlight and Open Calais in terms of F1-score and Mean Reciprocal Rank.

Keywords

Place name disambiguation Natural language processing LDA Wikipedia DBpedia Linked Data 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yiting Ju
    • 1
  • Benjamin Adams
    • 2
  • Krzysztof Janowicz
    • 1
  • Yingjie Hu
    • 3
  • Bo Yan
    • 1
  • Grant McKenzie
    • 4
  1. 1.STKO Lab, University of CaliforniaSanta BarbaraUSA
  2. 2.Centre for eResearch, The University of AucklandBerkeleyNew Zealand
  3. 3.Department of GeographyUniversity of TennesseeKnoxvilleUSA
  4. 4.Department of Geographical SciencesUniversity of MarylandCollege ParkUSA

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