Search Results for author:
Found 11 papers, 0 papers with code
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.
Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction
We perform a comparative study for automatic term extraction from domain-specific language using a PageRank model with different edge-weighting methods.
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction
We introduce SURel, a novel dataset with human-annotated meaning shifts between general-language and domain-specific contexts.
Fine-Grained Termhood Prediction for German Compound Terms Using Neural Networks
Automatic term identification and investigating the understandability of terms in a specialized domain are often treated as two separate lines of research.
A Laypeople Study on Terminology Identification across Domains and Task Definitions
This paper introduces a new dataset of term annotation.
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.
GhoSt-NN: A Representative Gold Standard of German Noun-Noun Compounds
This paper presents a novel gold standard of German noun-noun compounds (Ghost-NN) including 868 compounds annotated with corpus frequencies of the compounds and their constituents, productivity and ambiguity of the constituents, semantic relations between the constituents, and compositionality ratings of compound-constituent pairs.
