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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.
In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss’ multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.