Using foreign inclusion detection to improve parsing performance

Beatrice Alex; Amit Dubey; Frank Keller

Using foreign inclusion detection to improve parsing performance

Beatrice Alex
, Amit Dubey
, Frank Keller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Inclusions from other languages can be a significant source of errors for monolin-
gual parsers. We show this for English inclusions, which are sufficiently frequent to
present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and develop two methods for interfacing this approach with a state-of-the-art parser for German. An evaluation on the TIGER corpus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech tags.

Original language	English
Title of host publication	Proceedings of EMNLP-CoNLL 2007
Number of pages	10
Publication status	Published - 2007

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http://acl.ldc.upenn.edu/D/D07/D07-1016.pdf

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title = "Using foreign inclusion detection to improve parsing performance",

abstract = "Inclusions from other languages can be a significant source of errors for monolin- gual parsers. We show this for English inclusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and develop two methods for interfacing this approach with a state-of-the-art parser for German. An evaluation on the TIGER corpus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech tags.",

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T1 - Using foreign inclusion detection to improve parsing performance

AU - Alex, Beatrice

AU - Dubey, Amit

AU - Keller, Frank

PY - 2007

Y1 - 2007

N2 - Inclusions from other languages can be a significant source of errors for monolin- gual parsers. We show this for English inclusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and develop two methods for interfacing this approach with a state-of-the-art parser for German. An evaluation on the TIGER corpus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech tags.

AB - Inclusions from other languages can be a significant source of errors for monolin- gual parsers. We show this for English inclusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and develop two methods for interfacing this approach with a state-of-the-art parser for German. An evaluation on the TIGER corpus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion detection, and even outperforms a parser with access to gold standard part-of-speech tags.

KW - seer,

M3 - Conference contribution

BT - Proceedings of EMNLP-CoNLL 2007

ER -

Using foreign inclusion detection to improve parsing performance

Abstract

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