no code implementations • WS 2018 • Ina Roesiger, Maximilian K{\"o}per, Kim Anh Nguyen, Sabine Schulte im Walde
Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents.
no code implementations • NAACL 2018 • Eleri Aedmaa, Maximilian K{\"o}per, Sabine Schulte im Walde
This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i. e., Estonian.
no code implementations • NAACL 2018 • Maximilian K{\"o}per, Sabine Schulte im Walde
We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs.
no code implementations • SEMEVAL 2018 • Sabine Schulte im Walde, Maximilian K{\"o}per, Sylvia Springorum
This paper presents a collection to assess meaning components in German complex verbs, which frequently undergo meaning shifts.
no code implementations • WS 2017 • Maximilian K{\"o}per, Evgeny Kim, Roman Klinger
Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms.
no code implementations • WS 2017 • Maximilian K{\"o}per, Sabine Schulte im Walde
This paper compares a neural network DSM relying on textual co-occurrences with a multi-modal model integrating visual information.
no code implementations • EACL 2017 • Maximilian K{\"o}per, Sabine Schulte im Walde
Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level.
no code implementations • WS 2017 • Maximilian K{\"o}per, Sabine Schulte im Walde
Abstract words refer to things that can not be seen, heard, felt, smelled, or tasted as opposed to concrete words.
no code implementations • LREC 2016 • Maximilian K{\"o}per, Melanie Zai{\ss}, Qi Han, Steffen Koch, Sabine Schulte im Walde
Vector space models and distributional information are widely used in NLP.
no code implementations • LREC 2016 • Maximilian K{\"o}per, Sabine Schulte im Walde
This paper presents a collection of 350, 000 German lemmatised words, rated on four psycholinguistic affective attributes.
no code implementations • LREC 2014 • Maximilian K{\"o}per, Sabine Schulte im Walde
This paper addresses vector space models of prepositions, a notoriously ambiguous word class.
no code implementations • LREC 2014 • Jason Utt, Sylvia Springorum, Maximilian K{\"o}per, Sabine Schulte im Walde
This paper discusses an extension of the V-measure (Rosenberg and Hirschberg, 2007), an entropy-based cluster evaluation metric.