Mention detection crossing the language barrier

Mention detection crossing the language barrier (2008)

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by Imed Zitouni , Radu Florian

Venue:	In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP
Citations:	4 - 3 self

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Datum	Value	Source
TITLE	Mention detection crossing the language barrier	INFERENCE
AUTHOR NAME	Imed Zitouni	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM T.J. Watson Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	1101 Kitchawan Rd, Yorktown Heights, NY 10598	SVM HeaderParse 0.2
AUTHOR NAME	Radu Florian	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM T.J. Watson Research Center	SVM HeaderParse 0.2
AUTHOR ADDR	1101 Kitchawan Rd, Yorktown Heights, NY 10598	SVM HeaderParse 0.2
ABSTRACT	While significant effort has been put into annotating linguistic resources for several languages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagating information (specifically mention detection information) into such low resource languages from richer ones. Experiments run on three language pairs (Arabic-English, Chinese-English, and Spanish-English) show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance using various degrees of linguistic information in a statistical framework, results show that propagated features from English help improve the source-language system performance even when used in conjunction with all feature types built from the source language. The experiments also show that using propagated features in conjunction with lexicallyderived features only (as can be obtained directly from a mention annotated corpus) yields similar performance to using feature types derived from many linguistic resources. 1	SVM HeaderParse 0.2
YEAR	2008	INFERENCE
VENUE	In Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	30 found	ParsCit 1.0