LREC 2000 - Papers

LREC 2000 2^nd International Conference on Language Resources & Evaluation

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Title How To Evaluate and Compare Tagsets? A Proposal

Authors Dejean Herve (Seminar fur Sprachwissenschaft, Universitat Tubingen, dejean@sfs.nphil.uni-tuebingen.de)

Keywords

Session Session EP1 - Evaluation and Written Area

Abstract We propose a methodology which allows an evaluation of distributional qualities of a tagset and a comparison between tagsets. Evaluation of tagset is crucial since the task of tagging is often considered as one of the first tasks in language processing. The aim of tagging is to summarise as well as possible linguistic information for further processing such as syntactic parsing. The idea is to consider these further steps in order to evaluate a given tagset, and thus to measure the pertinence of the information provided by the tagset for these steps. For this purpose, a Machine Learning system, ALLiS, is used, whose goal is to learn phrase structures from bracketed corpora and to generate formal grammar which describes these structures. ALLiS learning is based on the detection of structural regularities. By this means, it can be pointed out some non-distributional behaviours of the tagset, and thus some of its weaknesses or its inadequacies.

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Title	How To Evaluate and Compare Tagsets? A Proposal
Authors	Dejean Herve (Seminar fur Sprachwissenschaft, Universitat Tubingen, dejean@sfs.nphil.uni-tuebingen.de)
Keywords
Session	Session EP1 - Evaluation and Written Area
Abstract	We propose a methodology which allows an evaluation of distributional qualities of a tagset and a comparison between tagsets. Evaluation of tagset is crucial since the task of tagging is often considered as one of the first tasks in language processing. The aim of tagging is to summarise as well as possible linguistic information for further processing such as syntactic parsing. The idea is to consider these further steps in order to evaluate a given tagset, and thus to measure the pertinence of the information provided by the tagset for these steps. For this purpose, a Machine Learning system, ALLiS, is used, whose goal is to learn phrase structures from bracketed corpora and to generate formal grammar which describes these structures. ALLiS learning is based on the detection of structural regularities. By this means, it can be pointed out some non-distributional behaviours of the tagset, and thus some of its weaknesses or its inadequacies.