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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/25b0e9be-4d0d-4877-b8d7-b0a3c5e3af25
- author
- Klang, Marcus
LU
and Nugues, Pierre
LU
- organization
- publishing date
- 2018-05
- 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
- 2022-05-03 02:46:35
@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}},
}