LREC 2006 - Proceedings sorted by papers

Title	Tagset Mapping and Statistical Training Data Cleaning-up
Authors	F. Pîrvan, D. Tufi
Abstract	The paper describes a general method (as well as its implementation and evaluation) for deriving mapping systems for different tagsets available in existing training corpora (gold standards) for a specific language. For each pair of corpora (tagged with different tagsets), one such mapping system is derived. This mapping system is then used to improve the tagging of each of the two corpora with the tagset of the other (this process will be called cross-tagging). By reapplying the algorithm to the newly obtained corpora, the accuracy of the underlying training corpora can also be improved. Furthermore, comparing the results with the gold standards makes it possible to assess the distributional adequacy of various tagsets used in processing the language in case. Unlike other methods, such as those reported in (Brants, 1995) or (Tufis & Dragomirescu, 2004), which assume a subsumption relation between the considered tagsets, and as such they aim at minimizing the tagsets by eliminating the feature-value redundancy, this method is applicable for completely unrelated tagsets. Although the experiments were focused on morpho-syntactic (POS) tagging, the method is applicable to other types of tagging as well.
Keywords	training corpora, tagging errors detection and correction, tagsets mapping
Full paper	Tagset Mapping and Statistical Training Data Cleaning-up

Title

Tagset Mapping and Statistical Training Data Cleaning-up

Authors

F. Pîrvan, D. Tufi

Abstract

The paper describes a general method (as well as its implementation and evaluation) for deriving mapping systems for different tagsets available in existing training corpora (gold standards) for a specific language. For each pair of corpora (tagged with different tagsets), one such mapping system is derived. This mapping system is then used to improve the tagging of each of the two corpora with the tagset of the other (this process will be called cross-tagging). By reapplying the algorithm to the newly obtained corpora, the accuracy of the underlying training corpora can also be improved. Furthermore, comparing the results with the gold standards makes it possible to assess the distributional adequacy of various tagsets used in processing the language in case. Unlike other methods, such as those reported in (Brants, 1995) or (Tufis & Dragomirescu, 2004), which assume a subsumption relation between the considered tagsets, and as such they aim at minimizing the tagsets by eliminating the feature-value redundancy, this method is applicable for completely unrelated tagsets. Although the experiments were focused on morpho-syntactic (POS) tagging, the method is applicable to other types of tagging as well.

Keywords

training corpora, tagging errors detection and correction, tagsets mapping

Full paper

Tagset Mapping and Statistical Training Data Cleaning-up