Jennifer Krisch


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A Lexical Resource for the Identification of “Weak Words” in German Specification Documents
Jennifer Krisch | Melanie Dick | Ronny Jauch | Ulrich Heid
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We report on the creation of a lexical resource for the identification of potentially unspecific or imprecise constructions in German requirements documentation from the car manufacturing industry. In requirements engineering, such expressions are called “weak words”: they are not sufficiently precise to ensure an unambiguous interpretation by the contractual partners, who for the definition of their cooperation, typically rely on specification documents (Melchisedech, 2000); an example are dimension adjectives, such as kurz or lang (‘short’, ‘long’) which need to be modified by adverbials indicating the exact duration, size etc. Contrary to standard practice in requirements engineering, where the identification of such weak words is merely based on stopword lists, we identify weak uses in context, by querying annotated text. The queries are part of the resource, as they define the conditions when a word use is weak. We evaluate the recognition of weak uses on our development corpus and on an unseen evaluation corpus, reaching stable F1-scores above 0.95.


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French and German Corpora for Audience-based Text Type Classification
Amalia Todirascu | Sebastian Padó | Jennifer Krisch | Max Kisselew | Ulrich Heid
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents some of the results of the CLASSYN project which investigated the classification of text according to audience-related text types. We describe the design principles and the properties of the French and German linguistically annotated corpora that we have created. We report on tools used to collect the data and on the quality of the syntactic annotation. The CLASSYN corpora comprise two text collections to investigate general text types difference between scientific and popular science text on the two domains of medical and computer science.