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Linking, Searching, and Visualizing Entities in Wikipedia

Klang, Marcus LU and Nugues, Pierre LU (2018) Language Resources and Evaluation Conference (LREC) p.3426-3432
Abstract
In this paper, we describe a new system to extract, index, search, and visualize entities in Wikipedia. To carry out the entity extraction, we designed a high-performance, multilingual, entity linker and we used a document model to store the resulting linguistic annotations. The entity linker, HEDWIG, extracts the mentions from text usinga string matching Engine and links them toentities with a combination of statistical rules and PageRank. The document model, Docforia (Klang and Nugues, 2017), consists of layers, where each layer is a sequence of ranges describing a specific annotation, here the entities. We evaluated HEDWIG with the TAC 2016 data and protocol (Ji and Nothman, 2016) and we reached the CEAFm scores of 70.0 on English, on... (More)
In this paper, we describe a new system to extract, index, search, and visualize entities in Wikipedia. To carry out the entity extraction, we designed a high-performance, multilingual, entity linker and we used a document model to store the resulting linguistic annotations. The entity linker, HEDWIG, extracts the mentions from text usinga string matching Engine and links them toentities with a combination of statistical rules and PageRank. The document model, Docforia (Klang and Nugues, 2017), consists of layers, where each layer is a sequence of ranges describing a specific annotation, here the entities. We evaluated HEDWIG with the TAC 2016 data and protocol (Ji and Nothman, 2016) and we reached the CEAFm scores of 70.0 on English, on 64.4 on Chinese, and 66.5 on Spanish. We applied the entity linker to the whole collection of English and Swedish articles of Wikipedia and we used Lucene to index the layers and a search module to interactively retrieve all the concordances of an entity in Wikipedia. The user can select and visualize the concordances in the articles or paragraphs. Contrary to classic text indexing, this system does not use strings to identify the entities but unique identifiers from Wikidata (Less)
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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
pages
3426 - 3432
conference name
Language Resources and Evaluation Conference (LREC)
conference location
Miyazaki, Japan
conference dates
2018-05-07 - 2018-05-12
external identifiers
  • scopus:85059879922
ISBN
979-10-95546-00-9
language
English
LU publication?
yes
id
25b0e9be-4d0d-4877-b8d7-b0a3c5e3af25
alternative location
http://www.lrec-conf.org/proceedings/lrec2018/summaries/93.html
date added to LUP
2018-05-08 11:26:25
date last changed
2020-01-13 08:12:45
@inproceedings{25b0e9be-4d0d-4877-b8d7-b0a3c5e3af25,
  abstract     = {In this paper, we describe a new system to extract, index, search, and visualize entities in Wikipedia. To carry out the entity extraction, we designed a high-performance, multilingual, entity linker and we used a document model to store the resulting linguistic annotations. The entity linker, HEDWIG, extracts the mentions from text usinga string matching Engine and links them toentities with a combination of statistical rules and PageRank. The document model, Docforia (Klang and Nugues, 2017), consists of layers, where each layer is a sequence of ranges describing a specific annotation, here the entities. We evaluated HEDWIG with the TAC 2016 data and protocol (Ji and Nothman, 2016) and we reached the CEAFm scores of 70.0 on English, on 64.4 on Chinese, and 66.5 on Spanish. We applied the entity linker to the whole collection of English and Swedish articles of Wikipedia and we used Lucene to index the layers and a search module to interactively retrieve all the concordances of an entity in Wikipedia. The user can select and visualize the concordances in the articles or paragraphs. Contrary to classic text indexing, this system does not use strings to identify the entities but unique identifiers from Wikidata},
  author       = {Klang, Marcus and Nugues, Pierre},
  booktitle    = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  isbn         = {979-10-95546-00-9},
  language     = {eng},
  pages        = {3426--3432},
  title        = {Linking, Searching, and Visualizing Entities in Wikipedia},
  url          = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/93.html},
  year         = {2018},
}