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and topsets are well developed. This is an example of the different deltas. Some additional examples of deltas and shorelines are shown in the figures 9 to 12. Stop 4a: Delta conglomerates of the Rissian LakeRosenheim at the quarry Grad Nagelfluhwerk, Brannenburg Stop 4b: Foresets of the delta conglomerates at quarry Anton Huber, BrannenburgThe Late Würmian Lake Rosenheim was not the first lake in this area. At this site conglomerated deltaic sediments of Rissian or older age (Wolff 1973) can be seen, which point to a lake level of around 520 m a.s.l.. and about 30 m higher than the lake level estimated for the Late Würmian successor. It is possible that the extension of this older lake reached almost to the city of Erding 55 km in the North, where fine grained Rissian lake sediments at an elevation lower than 510 m a.s.l. occur. Stop 5: The interglacial of Höhenmoos (MIS 7)In November 2009 the graben east of the village of Höhenmoos was examined in the course of the geological mapping (IOGI project of the Bavarian Geological Survey, Kunz

Recent work in parametric language learning has showed that even very small systems of linguistically plausible parameters pose very serious problems for error-driven and conservativt: learning algorithms. It has been argued that such problems may be solv,ecI by considering that different parameters may become available for resetting at different times, as an effect of biological maturation. This article prese.nts a general framework for studying the effects of the Maturation Hypothesis on the problem of language learning, parametrically conceived, and offers a general method for finding all maturational solutions (where some exist) for any parametric hypothesis space and any learning algorithm that differs from Gibson and Wexler's TLA only in the number of parameters that call be reset at each step. Implications for research in natural language acquisition are discussed in the concluding section.

We present an automatic approach to learning criteria for classifying the parts-of-speech used in lexical mappings. This will further automate our knowledge acquisition system for non-technical users. The criteria for the speech parts are based on the types of the denoted terms along with morphological and corpus-based clues. Associations among these and the parts-of-speech are learned using the lexical mappings contained in the Cyc knowledge base as training data. With over 30 speech parts to choose from, the classifier achieves good results (77.8% correct). Accurate results (93.0%) are achieved in the special case of the mass-count distinction for nouns. Comparable results are also obtained using OpenCyc (73.1% general and 88.4% mass-count). Collection: PhysicalDeviceMicrotheory: ArtifactGVocabularyMt isa: ExistingObjectType genls: Artifact ComplexPhysicalObject SolidTangibleProduct Microtheory: ProductGMt isa: ProductType

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