Rainer Osswald


2023

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Identifying Semantic Argument Types in Predication and Copredication Contexts: A Zero-Shot Cross-Lingual Approach
Deniz Ekin Yavas | Laura Kallmeyer | Rainer Osswald | Elisabetta Jezek | Marta Ricchiardi | Long Chen
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Identifying semantic argument types in predication contexts is not a straightforward task for several reasons, such as inherent polysemy, coercion, and copredication phenomena. In this paper, we train monolingual and multilingual classifiers with a zero-shot cross-lingual approach to identify semantic argument types in predications using pre-trained language models as feature extractors. We train classifiers for different semantic argument types and for both verbal and adjectival predications. Furthermore, we propose a method to detect copredication using these classifiers through identifying the argument semantic type targeted in different predications over the same noun in a sentence. We evaluate the performance of the method on copredication test data with Food•Event nouns for 5 languages.

2022

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A Frame-Based Model of Inherent Polysemy, Copredication and Argument Coercion
Chen Long | Laura Kallmeyer | Rainer Osswald
Proceedings of the Workshop on Cognitive Aspects of the Lexicon

The paper presents a frame-based model of inherently polysemous nouns (such as ‘book’, which denotes both a physical object and an informational content) in which the meaning facets are directly accessible via attributes and which also takes into account the semantic relations between the facets. Predication over meaning facets (as in ‘memorize the book’) is then modeled as targeting the value of the corresponding facet attribute while coercion (as in ‘finish the book’) is modeled via specific patterns that enrich the predication. We use a compositional framework whose basic components are lexicalized syntactic trees paired with semantic frames and in which frame unification is triggered by tree composition. The approach is applied to a variety of combinations of predications over meaning facets and coercions.

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RRGparbank: A Parallel Role and Reference Grammar Treebank
Tatiana Bladier | Kilian Evang | Valeria Generalova | Zahra Ghane | Laura Kallmeyer | Robin Möllemann | Natalia Moors | Rainer Osswald | Simon Petitjean
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper describes the first release of RRGparbank, a multilingual parallel treebank for Role and Reference Grammar (RRG) containing annotations of George Orwell’s novel 1984 and its translations. The release comprises the entire novel for English and a constructionally diverse and highly parallel sample (“seed”) for German, French and Russian. The paper gives an overview of annotation decisions that have been taken and describes the adopted treebanking methodology. Finally, as a possible application, a multilingual parser is trained on the treebank data. RRGparbank is one of the first resources to apply RRG to large amounts of real-world data. Furthermore, it enables comparative and typological corpus studies in RRG. And, finally, it creates new possibilities of data-driven NLP applications based on RRG.

2021

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Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)
Kilian Evang | Laura Kallmeyer | Rainer Osswald | Jakub Waszczuk | Torsten Zesch
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

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Proceedings of the ESSLLI 2021 Workshop on Computing Semantics with Types, Frames and Related Structures
Stergios Chatzikyriakidis | Rainer Osswald
Proceedings of the ESSLLI 2021 Workshop on Computing Semantics with Types, Frames and Related Structures

2020

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Automatic Extraction of Tree-Wrapping Grammars for Multiple Languages
Tatiana Bladier | Laura Kallmeyer | Rainer Osswald | Jakub Waszczuk
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

2019

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Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures
Rainer Osswald | Christian Retoré | Peter Sutton
Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures

2017

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Combining Predicate-Argument Structure and Operator Projection: Clause Structure in Role and Reference Grammar
Laura Kallmeyer | Rainer Osswald
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

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Modeling Quantification with Polysemous Nouns
Laura Kallmeyer | Rainer Osswald
Proceedings of the 12th International Conference on Computational Semantics (IWCS) — Short papers

2016

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Argument linking in LTAG: A constraint-based implementation with XMG
Laura Kallmeyer | Timm Lichte | Rainer Osswald | Simon Petitjean
Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+12)

2007

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Combining Contexts in Lexicon Learning for Semantic Parsing
Richard Socher | Chris Biemann | Rainer Osswald
Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA 2007)

2006

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Semantic Interpretation of Prepositions for NLP Applications
Sven Hartrumpf | Hermann Helbig | Rainer Osswald
Proceedings of the Third ACL-SIGSEM Workshop on Prepositions

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The Representation of German Prepositional Verbs in a Semantically Based Computer Lexicon
Rainer Osswald | Hermann Helbig | Sven Hartrumpf
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We describe the treatment of verbs with prepositional complements inHaGenLex, a semantically based computer lexicon for German.Prepositional verbs such as “bestehen auf” (insist on) subcategorize for a prepositional phrase where the preposition usually has no independent meaning of its own. The lexical semantic information inHaGenLex is specified by means of MultiNet, a full-fledged knowledge representation formalism, which proves to be particularly useful for representing the semantics of verbs with prepositional complements. We indicate how the semantic representation in HaGenLex can be used to define semantic classes of prepositional verbs and briefly discuss the relation of these classes to Levin's verb classes. Moreover, wepresent first results on the automatic identification of prepositionalverbs by corpus-based methods.