Advanced

Linking, Searching, and Visualizing Entities for the Swedish Wikipedia

Södergren, Anton; Klang, Marcus LU and Nugues, Pierre LU (2016) Sixth Swedish Language Technology Conference (SLTC 2016)
Abstract
In this paper, we describe a new system to extract, index, search, and visualize entities on Wikipedia. To carry out the extraction, we designed a high-performance entity linker and we used a document model to store the resulting linguistic annotations. The entity linker ,HERD, extracts the mentions from text using a string matching Engine and links the mto entities with a combination of rules, PageRank, and feature vectors based on the Wikipedia categories. The document model, Docforia, consists of layers, where each layer is a sequence of ranges describing a specific annotation,here thee ntities. We evaluated HERD with the ERD’14 protocol (Carmel et al., 2014) and we reached the competitive F1-score of 0.746 on the English development... (More)
In this paper, we describe a new system to extract, index, search, and visualize entities on Wikipedia. To carry out the extraction, we designed a high-performance entity linker and we used a document model to store the resulting linguistic annotations. The entity linker ,HERD, extracts the mentions from text using a string matching Engine and links the mto entities with a combination of rules, PageRank, and feature vectors based on the Wikipedia categories. The document model, Docforia, consists of layers, where each layer is a sequence of ranges describing a specific annotation,here thee ntities. We evaluated HERD with the ERD’14 protocol (Carmel et al., 2014) and we reached the competitive F1-score of 0.746 on the English development set. We applied HERD to the whole collection of Swedish articles of Wikipedia and we used Lucene to index the layers and a search module to interactively retrieve articles and metadata given a title, a phrase, or a property. The user can then select an entity and visualize concordance in articles or paragraphs. A demonstration of the entity search and visualization is available for Swedish at this address: http://vilde.cs.lth.se:9001/sv-herd/.
(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
Sixth Swedish Language Technology Conference (SLTC 2016)
language
English
LU publication?
yes
id
6c907110-bb62-47c4-868c-84f0570a3b5b
alternative location
http://www8.cs.umu.se/~johanna/sltc2016/abstracts/SLTC_2016_paper_8.pdf
date added to LUP
2017-01-11 17:03:53
date last changed
2017-01-13 11:26:38
@misc{6c907110-bb62-47c4-868c-84f0570a3b5b,
  abstract     = {In this paper, we describe a new system to extract, index, search, and visualize entities on Wikipedia. To carry out the extraction, we designed a high-performance entity linker and we used a document model to store the resulting linguistic annotations. The entity linker ,HERD, extracts  the mentions from text using a string matching Engine and links the mto entities with a combination of rules, PageRank, and feature vectors based on the Wikipedia categories. The document model, Docforia, consists of layers, where each layer is a sequence of ranges describing a specific annotation,here thee ntities. We evaluated HERD with the ERD’14 protocol (Carmel et al., 2014) and we reached the competitive F1-score of 0.746 on the English development set. We applied HERD to the whole collection of Swedish articles of Wikipedia and we used Lucene to index the layers and a search module to interactively retrieve articles and metadata given a title, a phrase, or a property. The user can then select an entity and visualize concordance in articles or paragraphs. A demonstration of the entity search and visualization is available for Swedish at this address: http://vilde.cs.lth.se:9001/sv-herd/.<br/>},
  author       = {Södergren, Anton and Klang, Marcus and Nugues, Pierre},
  language     = {eng},
  title        = {Linking, Searching, and Visualizing Entities for the Swedish Wikipedia},
  year         = {2016},
}