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We present a general framework for integrating annotations from different tools and tag sets. When annotating corpora at multiple linguistic levels, annotators may use different expert tools for different phenomena or types of annotation. These tools employ different data models and accompanying approaches to visualization, and they produce different output formats. For the purposes of uniformly processing these outputs, we developed a pivot format called PAULA, along with converters to and from tool formats. Different annotations are not only integrated at the level of data format, but are also joined on the level of conceptual representation. For this purpose, we introduce OLiA, an ontology of linguistic annotations that mediates between alternative tag sets that cover the same class of linguistic phenomena. All components are integrated in the linguistic information system ANNIS : Annotation tool output is converted to the pivot format PAULA and read into a database where the data can be visualized, queried, and evaluated across multiple layers. For cross-tag set querying and statistical evaluation, ANNIS uses the ontology of linguistic annotations. Finally, ANNIS is also tied to a machine learning component for semiautomatic annotation.
Experimental and quantitative research in the field of human language processing and production strongly depends on the quality of the underlying language material: beside its size, representativeness, variety and balance have been discussed as important factors which influence design, analysis and interpretation of experiments and their results. This volume brings together creators and users of both general purpose and specialized lexical resources which are used in psychology, psycholinguistics, neurolinguistics and cognitive research. It aims to be a forum to report experiences and results, review problems and discuss perspectives of any linguistic data used in the field.