Marek Kubis


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Geometric Deep Learning Models for Linking Character Names in Novels
Marek Kubis
Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

The paper investigates the impact of using geometric deep learning models on the performance of a character name linking system. The neural models that contain graph convolutional layers are confronted with the models that include conventional fully connected layers. The evaluation is performed with respect to the perfect name boundaries obtained from the test set and in a more demanding end-to-end setting where the character name linking system is preceded by a named entity recognizer.


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EUDAMU at SemEval-2017 Task 11: Action Ranking and Type Matching for End-User Development
Marek Kubis | Paweł Skórzewski | Tomasz Ziętkiewicz
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

The paper describes a system for end-user development using natural language. Our approach uses a ranking model to identify the actions to be executed followed by reference and parameter matching models to select parameter values that should be set for the given commands. We discuss the results of evaluation and possible improvements for future work.


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PolNet — Polish WordNet: Data and Tools
Zygmunt Vetulani | Marek Kubis | Tomasz Obrębski
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presents the PolNet-Polish WordNet project which aims at building a linguistically oriented ontology for Polish compatible with other WordNet projects such as Princeton WordNet, EuroWordNet and other similarly organized ontologies. The main idea behind this kind of ontologies is to use words related by synonymy to construct formal representations of concepts. In the paper we sketch the PolNet project methodology and implementation. We present data obtained so far, as well as the WQuery tool for querying and maintaining PolNet. WQuery is a query language that make use of data types based on synsets, word senses and various semantic relations which occur in wordnet-like lexical databases. The tool is particularly useful to deal with complex querying tasks like searching for cycles in semantic relations, finding isolated synsets or computing overall statistics. Both data and tools presented in this paper have been applied within an advanced AI system POLINT-112-SMS with emulated natural language competence, where they are used in the understanding subsystem.