In this paper, we analyse and compare several correction methods of knowledge resources with the purpose of improving the abilities of systems that require commonsense reasoning with the least possible human-effort.To this end, we cross-check the WordNet meronymy relation member against the knowledge encoded in a SUMO-based first-order logic ontology on the basis of the mapping between WordNet and SUMO.In particular, we focus on the knowledge in WordNet regarding the taxonomy of animals and plants.Despite being created manually, these knowledge resources -WordNet, SUMO and their mapping-are not free of errors and discrepancies.Thus, we propose three correction methods by semi-automatically improving the alignment between WordNet and SUMO, by performing some few corrections in SUMO and by combining the above two strategies.The evaluation of each method includes the required human-effort and the achieved improvement on unseen data from the WebChild project, that is tested using first-order logic automated theorem provers.

Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language.Nowadays, with the use of large amounts of data gathered from social media, other varieties and registers need to be processed, which may present other challenges and difficulties.In this work, we focus on English and we present a preliminary analysis by comparing the Twitter-AAE corpus, which is annotated for ethnicity, and WordNet by quantifying and explaining the online language that Word-Net misses.

We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and their mapping.The objective is to provide a better assessment of the quality of both the benchmark and the involved knowledge resources for advanced commonsense reasoning tasks.By means of this analysis, we are able to detect some knowledge misalignments, mapping errors and lack of knowledge and resources.Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.

When teaching language for specific purposes (LSP) linguistic resources are needed to help students understand and write specialised texts.As building a lexical resource is costly, we explore the use of wordnets to represent the terms that can be found in particular textual domains.In order to gather the terms to be included in wordnets, we propose a textual genre approach, that leads us to introduce a new relation term used in to link all the possible terms/synsets that can appear in a text to the synset of the textual genre.This way, students can use wordnet as dictionary or thesaurus when writing specialised texts.We explain our approach by means of the logbooks and terms in Basque.A side effect of this works is also enriching the wordnets with new variants and synsets.