Max Kisselew


2017

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Modeling Derivational Morphology in Ukrainian
Mariia Melymuka | Gabriella Lapesa | Max Kisselew | Sebastian Padó
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

2016

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Predictability of Distributional Semantics in Derivational Word Formation
Sebastian Padó | Aurélie Herbelot | Max Kisselew | Jan Šnajder
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions. Their performance on semi-compositional word formation process of (morphological) derivation, however, has been extremely variable, with no large-scale empirical investigation to date. This paper fills that gap, performing an analysis of CDSM predictions on a large dataset (over 30,000 German derivationally related word pairs). We use linear regression models to analyze CDSM performance and obtain insights into the linguistic factors that influence how predictable the distributional context of a derived word is going to be. We identify various such factors, notably part of speech, argument structure, and semantic regularity.

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Predicting the Direction of Derivation in English Conversion
Max Kisselew | Laura Rimell | Alexis Palmer | Sebastian Padó
Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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GhoSt-PV: A Representative Gold Standard of German Particle Verbs
Stefan Bott | Nana Khvtisavrishvili | Max Kisselew | Sabine Schulte im Walde
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

German particle verbs represent a frequent type of multi-word-expression that forms a highly productive paradigm in the lexicon. Similarly to other multi-word expressions, particle verbs exhibit various levels of compositionality. One of the major obstacles for the study of compositionality is the lack of representative gold standards of human ratings. In order to address this bottleneck, this paper presents such a gold standard data set containing 400 randomly selected German particle verbs. It is balanced across several particle types and three frequency bands, and accomplished by human ratings on the degree of semantic compositionality.

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Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling
Maximilian Köper | Sabine Schulte im Walde | Max Kisselew | Sebastian Padó
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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Obtaining a Better Understanding of Distributional Models of German Derivational Morphology
Max Kisselew | Sebastian Padó | Alexis Palmer | Jan Šnajder
Proceedings of the 11th International Conference on Computational Semantics

2013

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Munich-Edinburgh-Stuttgart Submissions at WMT13: Morphological and Syntactic Processing for SMT
Marion Weller | Max Kisselew | Svetlana Smekalova | Alexander Fraser | Helmut Schmid | Nadir Durrani | Hassan Sajjad | Richárd Farkas
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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French and German Corpora for Audience-based Text Type Classification
Amalia Todirascu | Sebastian Padó | Jennifer Krisch | Max Kisselew | Ulrich Heid
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper presents some of the results of the CLASSYN project which investigated the classification of text according to audience-related text types. We describe the design principles and the properties of the French and German linguistically annotated corpora that we have created. We report on tools used to collect the data and on the quality of the syntactic annotation. The CLASSYN corpora comprise two text collections to investigate general text types difference between scientific and popular science text on the two domains of medical and computer science.