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Lexical chaining has become an important part of many NLP tasks. However, the goodness of a chaining process and hence its annotation output depends on the quality of the chaining resource. Therefore, a framework for chaining is needed which integrates divergent resources in order to balance their deficits and to compare their strengths and weaknesses. In this paper we present an application that incorporates the framework of a meta model of lexical chaining exemplified on three resources and its generalized exchange format.
Researchers in many disciplines, sometimes working in close cooperation, have been concerned with modeling textual data in order to account for texts as the prime information unit of written communication. The list of disciplines includes computer science and linguistics as well as more specialized disciplines like computational linguistics and text technology. What many of these efforts have in common is the aim to model textual data by means of abstract data types or data structures that support at least the semi-automatic processing of texts in any area of written communication.