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

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Title Using a Large Set of EAGLES-compliant Morpho-syntactic Descriptors as a Tagset for Probabilistic Tagging

Authors Tufis Dan (RACAI-Romanian Academy 13, “13 Septembrie”, Ro-74311, Bucharest 5, Romania, email:tufis@valhalla.racai.ro)

Keywords Combined Classifiers, Evaluation, Standardization, Tagset Design, Tiered Tagging

Session Session WP5 - Corpus Tagging

Abstract The paper presents one way of reconciling data sparseness with the requirement of high accuracy tagging in terms of fine-grained tagsets. For lexicon encoding, EAGLES elaborated a set of recommendations aimed at covering multilingual requirements and therefore resulted in a large number of features and possible values. Such an encoding, used for tagging purposes, would lead to very large tagsets. For instance, our EAGLES-compliant lexicon required a set of about 1000 morpho-syntactic description codes (MSDs) which after considering some systematic syncretic phenomena, was reduced to a set of 614 MSDs. Building reliable language models (LMs) for this tagset would require unrealistically large training data (hand annotated/validated). Our solution was to design a hidden reduced tagset and use it in building various LMs. The underlying tagger uses these LMs to tag a new text in as many variants as LMs are available. The tag differences between these variants are processed by a combiner which chooses the most likely tags. In the end, the tagged text is subject to a conversion process that maps the tags from the reduced tagset onto the more informative tags from the large tagset. We describe this processing chain and provide a detailed evaluation of the results.

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Title	Using a Large Set of EAGLES-compliant Morpho-syntactic Descriptors as a Tagset for Probabilistic Tagging
Authors	Tufis Dan (RACAI-Romanian Academy 13, “13 Septembrie”, Ro-74311, Bucharest 5, Romania, email:tufis@valhalla.racai.ro)
Keywords	Combined Classifiers, Evaluation, Standardization, Tagset Design, Tiered Tagging
Session	Session WP5 - Corpus Tagging
Abstract	The paper presents one way of reconciling data sparseness with the requirement of high accuracy tagging in terms of fine-grained tagsets. For lexicon encoding, EAGLES elaborated a set of recommendations aimed at covering multilingual requirements and therefore resulted in a large number of features and possible values. Such an encoding, used for tagging purposes, would lead to very large tagsets. For instance, our EAGLES-compliant lexicon required a set of about 1000 morpho-syntactic description codes (MSDs) which after considering some systematic syncretic phenomena, was reduced to a set of 614 MSDs. Building reliable language models (LMs) for this tagset would require unrealistically large training data (hand annotated/validated). Our solution was to design a hidden reduced tagset and use it in building various LMs. The underlying tagger uses these LMs to tag a new text in as many variants as LMs are available. The tag differences between these variants are processed by a combiner which chooses the most likely tags. In the end, the tagged text is subject to a conversion process that maps the tags from the reduced tagset onto the more informative tags from the large tagset. We describe this processing chain and provide a detailed evaluation of the results.