activity
20182020
most citedComputational linguistic assessment of textbook and online learning media by means of threshold concepts in business education

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20201 cited

Computational linguistic assessment of textbook and online learning media by means of threshold concepts in business education

Andy Lücking, Sebastian Brückner, Giuseppe Abrami +2

Threshold concepts are key terms in domain-based knowledge acquisition. They are regarded as building blocks of the conceptual development of domain knowledge within particular lea…

cs.CL20201 cited

The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphs

Alexander Mehler, Bernhard Jussen, Tim Geelhaar +5

In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing…

cs.CL2020

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

Alexander Mehler, Rüdiger Gleim, Regina Gaitsch +2

Are nearby places (e.g. cities) described by related words? In this article we transfer this research question in the field of lexical encoding of geographic information onto the l…

cs.CL2019

When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish

Manuel Stoeckel, Wahed Hemati, Alexander Mehler

The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically rel…

cs.CL2019

SenseFitting: Sense Level Semantic Specialization of Word Embeddings for Word Sense Disambiguation

Manuel Stoeckel, Sajawel Ahmed, Alexander Mehler

We introduce a neural network-based system of Word Sense Disambiguation (WSD) for German that is based on SenseFitting, a novel method for optimizing WSD. We outperform knowledge-b…

cs.CL2018

Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora

Sajawel Ahmed, Alexander Mehler

This study improves the performance of neural named entity recognition by a margin of up to 11% in F-score on the example of a low-resource language like German, thereby outperform…