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Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains
Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts.
Predicting Degrees of Technicality in Automatic Terminology Extraction
While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied.
A Domain-Specific Dataset of Difficulty Ratings for German Noun Compounds in the Domains DIY, Cooking and Automotive
We present a dataset with difficulty ratings for 1, 030 German closed noun compounds extracted from domain-specific texts for do-it-ourself (DIY), cooking and automotive.
Evaluating the Reliability and Interaction of Recursively Used Feature Classes for Terminology Extraction
Feature design and selection is a crucial aspect when treating terminology extraction as a machine learning classification problem.
Acquisition of semantic relations between terms: how far can we get with standard NLP tools?
The extraction of data exemplifying relations between terms can make use, at least to a large extent, of techniques that are similar to those used in standard hybrid term candidate extraction, namely basic corpus analysis tools (e. g. tagging, lemmatization, parsing), as well as morphological analysis of complex words (compounds and derived items).
