This paper presents XeraWord, an innovative tool for automatically generating nominal phrases. XeraWord can be used for different tasks, ranging from teaching languages to the creation of examples in lexicography, or even for the development of resources for natural language processing. In this area, Xera was the first experiment, allowing the automatic generation of nominal phrases in three languages: German, French and Spanish. This tool was extended to support other languages, namely, Portuguese and Galician.

We start by presenting the theory behind the development of Xera and its new version, XeraWord, namely, the applied base methodology, and the natural language processing resources used to support it. Then, TraduWord, a tool specifically developed to construct resources for new languages, is presented. This tool allows the semi-automatic translation of the data required for the nominal phrase generation. For this, we discuss its advantages and disadvantages, analysing the quality of the translated resources, as well as the amount of manual work required to validate and correct these resources.

This paper introduces techniques to integrate WordNet into a Fuzzy Logic Programming system. Since WordNet relates words but does not give graded information on the relation between them, we have implemented standard similarity measures and new directives allowing the proximity equations linking two words to be generated with an approximation degree. Proximity equations are the key syntactic structures which, in addition to a weak unification algorithm, make a flexible query-answering process possible in this kind of programming language. This addition widens the scope of Fuzzy Logic Programming, allowing certain forms of lexical reasoning, and reinforcing Natural Language Processing (NLP) applications.

Benchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the literature should have more to say than it does about the connection between benchmarks and goals. Is optimizing P@10 on WN18RR likely to produce more complete knowledge graphs? Is MUSE likely to improve Machine Translation?