Extracting parallel fragments from comparable corpora for data-to-text generation

Anja Belz; Eric Kow

doi:10.1.1.180.3640

Extracting parallel fragments from comparable corpora for data-to-text generation

Anja Belz, Eric Kow

University of Brighton

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

Abstract

Building NLG systems, in particular statistical ones, requires parallel data (paired inputs and outputs) which do not generally occur naturally. In this paper, we investigate the idea of automatically extracting parallel resources for data-to-text generation from comparable corpora obtained from the Web. We describe our comparable corpus of data and texts relating to British hills and the techniques for extracting paired input/output fragments we have developed so far.

Original language	English
Title of host publication	Proceedings of 6th International Natural Language Generation Conference (INLG'10)
Place of Publication	Stroudsburg, PA, USA
Publisher	Association for Computational Linguistics
Pages	167-171
Number of pages	5
DOIs	https://doi.org/10.1.1.180.3640
Publication status	Published - 1 Jan 2010
Event	Proceedings of 6th International Natural Language Generation Conference (INLG'10) - Dublin, Ireland Duration: 1 Jan 2010 → …

Conference

Conference	Proceedings of 6th International Natural Language Generation Conference (INLG'10)
Period	1/01/10 → …

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http://dl.acm.org/citation.cfm?id=1873738.1873760Licence: Unspecified

Cite this

@inproceedings{a9f49c8043bd4d9dabdc775704811f07,

title = "Extracting parallel fragments from comparable corpora for data-to-text generation",

abstract = "Building NLG systems, in particular statistical ones, requires parallel data (paired inputs and outputs) which do not generally occur naturally. In this paper, we investigate the idea of automatically extracting parallel resources for data-to-text generation from comparable corpora obtained from the Web. We describe our comparable corpus of data and texts relating to British hills and the techniques for extracting paired input/output fragments we have developed so far.",

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language = "English",

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booktitle = "Proceedings of 6th International Natural Language Generation Conference (INLG'10)",

publisher = "Association for Computational Linguistics",

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Belz, A & Kow, E 2010, Extracting parallel fragments from comparable corpora for data-to-text generation. in Proceedings of 6th International Natural Language Generation Conference (INLG'10). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 167-171, Proceedings of 6th International Natural Language Generation Conference (INLG'10), 1/01/10. https://doi.org/10.1.1.180.3640

Extracting parallel fragments from comparable corpora for data-to-text generation. / Belz, Anja; Kow, Eric.
Proceedings of 6th International Natural Language Generation Conference (INLG'10). Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. p. 167-171.

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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AU - Kow, Eric

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AB - Building NLG systems, in particular statistical ones, requires parallel data (paired inputs and outputs) which do not generally occur naturally. In this paper, we investigate the idea of automatically extracting parallel resources for data-to-text generation from comparable corpora obtained from the Web. We describe our comparable corpus of data and texts relating to British hills and the techniques for extracting paired input/output fragments we have developed so far.

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DO - 10.1.1.180.3640

M3 - Conference contribution with ISSN or ISBN

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BT - Proceedings of 6th International Natural Language Generation Conference (INLG'10)

PB - Association for Computational Linguistics

CY - Stroudsburg, PA, USA

T2 - Proceedings of 6th International Natural Language Generation Conference (INLG'10)

Y2 - 1 January 2010

ER -

Extracting parallel fragments from comparable corpora for data-to-text generation

Abstract

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