research-article

Named entity similarity computation: The case of social event entities

Authors:

Christian Sallaberry,

Annig Le Parc-LacayrelleAuthors Info & Claims

GIR'17: Proceedings of the 11th Workshop on Geographic Information Retrieval

Article No.: 9, Pages 1 - 8

https://doi.org/10.1145/3155902.3155903

Published: 30 November 2017 Publication History

Abstract

We consider events as complex named entities, recursively consisting of simple named entities (people, places, organizations, dates, etc.) and/or other complex named entities. We have developed a processing chain dedicated to extracting, indexing and searching for social events in Web pages. In this context, we are proposing a generic similarity computation function that targets any type of complex named entity. In this paper, we implement this function according to three approaches which we shall describe and then experiment on a set of social events.

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Cited By

Wang BFei TKang YLi MDu QHan MDong N(2020)Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context windowPLOS ONE10.1371/journal.pone.023634715:7(e0236347)Online publication date: 23-Jul-2020
https://doi.org/10.1371/journal.pone.0236347

Index Terms

Named entity similarity computation: The case of social event entities
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Environment-specific retrieval
        Web and social media search
  2. World Wide Web
    1. Web mining
      1. Data extraction and integration
    2. Web searching and information discovery
      1. Web search engines
        Web indexing

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GIR'17: Proceedings of the 11th Workshop on Geographic Information Retrieval

November 2017

64 pages

ISBN:9781450353380

DOI:10.1145/3155902

Copyright © 2017 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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New York, NY, United States

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Published: 30 November 2017

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GIR'17

GIR'17: 11th Workshop on Geographic Information Retrieval

November 30 - December 1, 2017

Heidelberg, Germany

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GIR'17 Paper Acceptance Rate 11 of 13 submissions, 85%;

Overall Acceptance Rate 46 of 61 submissions, 75%

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Cited By

Wang BFei TKang YLi MDu QHan MDong N(2020)Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context windowPLOS ONE10.1371/journal.pone.023634715:7(e0236347)Online publication date: 23-Jul-2020
https://doi.org/10.1371/journal.pone.0236347

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