Constructing large proposition databases

Peter Exner; Pierre Nugues

Constructing large proposition databases

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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Abstract

With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. This paper describes the creation of multilingual proposition databases using generic semantic dependency parsing. Using Wikipedia, we extracted, processed, clustered, and evaluated a large number of propositions. We built an architecture to provide a complete pipeline dealing with the input of text, extraction of knowledge, storage, and presentation of the resulting propositions

Original language	English
Title of host publication	Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)
Publisher	European Language Resources Association
Pages	3836-3839
ISBN (Electronic)	978-2-9517408-7-7
Publication status	Published - 2012
Event	The eighth international conference on Language Resources and Evaluation (LREC 2012) - Istanbul, Turkey Duration: 2012 May 21 → 2012 May 27

Conference

Conference	The eighth international conference on Language Resources and Evaluation (LREC 2012)
Country/Territory	Turkey
City	Istanbul
Period	2012/05/21 → 2012/05/27

Subject classification (UKÄ)

Computer Sciences

Free keywords

Knowledge Discovery/Representation
Information Extraction
Information Retrieval
Semantics

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lrec2012_proposition

http://www.lrec-conf.org/proceedings/lrec2012/pdf/452_Paper.pdfLicence: Unspecified

Cite this

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title = "Constructing large proposition databases",

abstract = "With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. This paper describes the creation of multilingual proposition databases using generic semantic dependency parsing. Using Wikipedia, we extracted, processed, clustered, and evaluated a large number of propositions. We built an architecture to provide a complete pipeline dealing with the input of text, extraction of knowledge, storage, and presentation of the resulting propositions",

keywords = "Knowledge Discovery/Representation, Information Extraction, Information Retrieval, Semantics",

author = "Peter Exner and Pierre Nugues",

year = "2012",

language = "English",

pages = "3836--3839",

booktitle = "Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)",

publisher = "European Language Resources Association",

address = "France",

note = "The eighth international conference on Language Resources and Evaluation (LREC 2012) ; Conference date: 21-05-2012 Through 27-05-2012",

}

Exner, P & Nugues, P 2012, Constructing large proposition databases. in Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12). European Language Resources Association, pp. 3836-3839, The eighth international conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey, 2012/05/21. <http://www.lrec-conf.org/proceedings/lrec2012/pdf/452_Paper.pdf>

TY - GEN

T1 - Constructing large proposition databases

AU - Exner, Peter

AU - Nugues, Pierre

PY - 2012

Y1 - 2012

N2 - With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. This paper describes the creation of multilingual proposition databases using generic semantic dependency parsing. Using Wikipedia, we extracted, processed, clustered, and evaluated a large number of propositions. We built an architecture to provide a complete pipeline dealing with the input of text, extraction of knowledge, storage, and presentation of the resulting propositions

AB - With the advent of massive online encyclopedic corpora such as Wikipedia, it has become possible to apply a systematic analysis to a wide range of documents covering a significant part of human knowledge. Using semantic parsers, it has become possible to extract such knowledge in the form of propositions (predicate―argument structures) and build large proposition databases from these documents. This paper describes the creation of multilingual proposition databases using generic semantic dependency parsing. Using Wikipedia, we extracted, processed, clustered, and evaluated a large number of propositions. We built an architecture to provide a complete pipeline dealing with the input of text, extraction of knowledge, storage, and presentation of the resulting propositions

KW - Knowledge Discovery/Representation

KW - Information Extraction

KW - Information Retrieval

KW - Semantics

UR - https://www.scopus.com/pages/publications/84973248631

M3 - Paper in conference proceeding

SP - 3836

EP - 3839

BT - Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)

PB - European Language Resources Association

T2 - The eighth international conference on Language Resources and Evaluation (LREC 2012)

Y2 - 21 May 2012 through 27 May 2012

ER -

Constructing large proposition databases

Abstract

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