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In this project, we have investigated the use of advanced machine learning methods, specifically fine-tuned large language models, for pre-annotating data for a lexical extension task, namely adding descriptive words (verbs) to an existing (but incomplete, as of yet) ontology of event types. Several research questions have been focused on, from the investigation of a possible heuristics to provide at least hints to annotators which verbs to include and which are outside the current version of the ontology, to the possible use of the automatic scores to help the annotators to be more efficient in finding a threshold for identifying verbs that cannot be assigned to any existing class and therefore they are to be used as seeds for a new class. We have also carefully examined the correlation of the automatic scores with the human annotation. While the correlation turned out to be strong, its influence on the annotation proper is modest due to its near linearity, even though the mere fact of such pre-annotation leads to relatively short annotation times., Comment: Accepted to LAW-XVII @ ACL 2023

We present an extension of the SynSemClass event-type ontology, originally conceived as a bilingual Czech-English resource. We added German entries to the classes representing the concepts of the ontology. Having a different starting point than the original work (unannotated parallel corpus without links to a valency lexicon and, of course, different existing lexical resources), it was a challenge to adapt the annotation guidelines, the data model and the tools used for the original version. We describe the process and results of working in such a setup. We also show the next steps to adapt the annotation process, data structures and formats and tools necessary to make the addition of a new language in the future more smooth and efficient, and possibly to allow for various teams to work on SynSemClass extensions to many languages concurrently. We also present the latest release which contains the results of adding German, freely available for download as well as for online access.

LLOD (Linguistic Linked Open Data) is a generic name for a set of mutually connected language resources, using ontological relations. The connections between concepts and between concepts and their expressions in natural language make them suitable for both research and industrial applications in the area of content analysis, natural language understanding, (language- and knowledge-based) inferencing and other tasks. In the presented task, the concrete work will be on converting the SynSemClass project dataset (in part as a result of a previous Humane AI Net microproject called META-O-NLU) into LLOD, connecting it to the huge amount or interlinked data already available. A partner is involved in the Prêt-à-LLOD H2020 project, making this project synergistic in nature and multiplicative in terms of results in previous projects. Partners are also involved in the COST Action “European network for Web-centered linguistic data science” (NexusLinguarum).

This report presents a guideline for including German synonyms into the multilingual SynSemClass lexicon.

While studying verbal synonymy, we have investigated the relation between syntax and semantics in hope that the exploration of this relationship will help us to get more insight into the question of synonymy as the relationship relating (similar) meanings between different lexemes. Most synonym lexicons (Wordnets and similar thesauri) are based on an intuition about the similarity of word meanings, or on notions like “semantic roles.” In some cases, syntax is also taken into account, but we have found no annotation and/or evaluation experiment to see how strongly can syntax contribute to synonym specification. We have prepared an annotation experiment for which we have used two treebanks (Czech and English) from the Universal Dependencies (UD) set of parallel corpora (PUDs) in order to see how strong correlation exists between syntax and the assignment of verbs in context to pre-determined (bilingual) classes of synonyms. The resulting statistics confirmed that while syntax does support decisions about synonymy, such support is not strong enough and that more semantic criteria are indeed necessary. The results of the annotation will also help to further improve rules and specifications for creating synonymous classes. Moreover, we have collected evidence that the annotation setup that we have used can identify synonym classes to be merged, and the resulting data (which we plan to publish openly) can possibly serve for the evaluation of automatic methods used in this area.