no code implementations • RepL4NLP (ACL) 2022 • Christian Wartena
In this paper we investigate how concreteness and abstractness are represented in word embedding spaces.
no code implementations • WS 2019 • Frieda Josi, Christian Wartena, Ulrich Heid
For the analysis of contract texts, validated model texts, such as model clauses, can be used to identify reused contract clauses.
no code implementations • WS 2019 • Christian Wartena, S, Uwe er, Christiane Patzelt
We present a simple method to find topics in user reviews that accompany ratings for products or services.
no code implementations • WS 2019 • Jean Charbonnier, Christian Wartena
Concreteness of words has been studied extensively in psycholinguistic literature.
no code implementations • COLING 2018 • Jean Charbonnier, Christian Wartena
Scientific papers from all disciplines contain many abbreviations and acronyms.
no code implementations • COLING 2016 • Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme
Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
no code implementations • WS 2016 • Christian Wartena, Rosa Tsegaye Aga
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation.
no code implementations • LREC 2016 • Rosa Tsegaye Aga, Christian Wartena, Lucas Drumond, Lars Schmidt-Thieme
The similarity of words can be computed by comparing their feature vectors.