@inproceedings{syed-etal-2010-automatic,
title = "Automatic Discovery of Semantic Relations using {M}ind{N}et",
author = "Syed, Zareen and
Viegas, Evelyne and
Parastatidis, Savas",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/78_Paper.pdf",
abstract = "Information extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ''``People born-in Country'''') from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new model to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.",
}
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<abstract>Information extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ”“People born-in Country””) from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new model to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.</abstract>
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%0 Conference Proceedings
%T Automatic Discovery of Semantic Relations using MindNet
%A Syed, Zareen
%A Viegas, Evelyne
%A Parastatidis, Savas
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F syed-etal-2010-automatic
%X Information extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ”“People born-in Country””) from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new model to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/78_Paper.pdf
Markdown (Informal)
[Automatic Discovery of Semantic Relations using MindNet](http://www.lrec-conf.org/proceedings/lrec2010/pdf/78_Paper.pdf) (Syed et al., LREC 2010)
ACL
- Zareen Syed, Evelyne Viegas, and Savas Parastatidis. 2010. Automatic Discovery of Semantic Relations using MindNet. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).