Automatically Acquiring Fine-Grained Information Status Distinctions in German

201265 citationsJournal Article

Authors

Aoife Cahill · Educational Testing Service

Arndt Riester · Institut für Mikroelektronik Stuttgart

Abstract

We present a model for automatically predicting information status labels for German referring expressions. We train a CRF on manually annotated phrases, and predict a fine-grained set of labels. We achieve an accuracy score of 69.56 % on our most detailed label set, 76.62% when gold standard coreference is available. 1

Topics & Keywords

Natural Language Processing Techniques Topic Modeling Text Readability and Simplification

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Automatically Acquiring Fine-Grained Information Status Distinctions in German

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Abstract

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