In this paper we report on a new Danish lexical initiative, the Central Word Register for Danish, (COR), which aims at providing an open-source, well curated and large-coverage lexicon for AI purposes.The semantic part of the lexicon (COR-S) relies to a large extent on the lexical-semantic information provided in the Danish wordnet, DanNet.However, we have taken the opportunity to evaluate and curate the wordnet information while compiling the new resource.Some information types have been simplified and more systematically curated.This is the case for the hyponymy relations, the ontological typing, and the sense inventory, i.e. the treatment of polysemy, including systematic polysemy.

Systematic polysemy is a well-known linguistic phenomenon where a group of lemmas follow the same polysemy pattern.However, when compiling a lexical resource like a wordnet, a problem arises regarding when to underspecify the two (or more) meanings by one (complex) sense and when to systematically split into separate senses.In this work, we present an extensive analysis of the systematic polysemy patterns in Danish, and in our preliminary study, we examine a subset of these with experiments on human intuition and contextual embeddings.The aim of this preparatory work is to enable future guidelines for each polysemy type.In the future, we hope to expand this approach and thereby hopefully obtain a sense inventory which is distributionally verified and thereby more suitable for NLP.

The paper describes work in progress in the DanNet2 project financed by the Carlsberg Foundation.The project aim is to extend the original Danish wordnet, DanNet, in several ways.Main focus is on extension of the coverage and description of the adjectives, a part of speech that was rather sparsely described in the original wordnet.We describe the methodology and initial work of semiautomatically transferring adjectives from the Danish Thesaurus to the wordnet with the aim of easily enlarging the coverage from 3,000 to approx.13,000 adjectival synsets.Transfer is performed by manually encoding all missing adjectival subsection headwords from the thesaurus and thereafter employing a semiautomatic procedure where adjectives from the same subsection are transferred to the wordnet as either 1) near synonyms to the section's headword, 2) hyponyms to the section's headword, or 3) as members of the same synset as the headword.We also discuss how to deal with the problem of multiple representations of the same sense in the thesaurus, and present other types of information from the thesaurus that we plan to integrate, such as thematic and sentiment information.

In this paper we describe the merge of the Danish wordnet, DanNet, with Princeton Wordnet applying a two-step approach.We first link from the English Princeton core to Danish (5,000 base concepts) and then proceed to linking the rest of the Danish vocabulary to English, thus going from Danish to English.Since the Danish wordnet is built bottom-up from Danish lexica and corpora, all taxonomies are monolingually based and thus not necessarily directly compatible with the coverage and structure of the Princeton WordNet.This fact proves to pose some challenges to the linking procedure since a considerable number of the links cannot be realised via the preferred crosslanguage synonym link which implies a more or less precise correlation between the two concepts.Instead, a subpart of the links are realised through near synonym or hyponymy links to compensate for the fact that no precise translation can be found in the target resource.The tool WordnetLoom is currently used for manual linking but procedures for a more automatic procedure in future is discussed.We conclude that the two resources actually differ from each other quite more than expected, both vocabulary-and structure-wise.

Our aim is to develop principled methods for sense clustering which can make existing lexical resources practically useful in NLPnot too fine-grained to be operational and yet finegrained enough to be worth the trouble.Where traditional dictionaries have a highly structured sense inventory typically describing the vocabulary by means of main-and subsenses, wordnets are generally fine-grained and unstructured.We present a series of clustering and annotation experiments with 10 of the most polysemous nouns in Danish.We combine the structured information of a traditional Danish dictionary with the ontological types found in the Danish wordnet, DanNet.This constellation enables us to automatically cluster senses in a principled way and improve inter-annotator agreement and wsd performance.

In this article we present an expansion of the supersense inventory.All new supersenses are extensions of members of the current inventory, which we postulate by identifying semantically coherent groups of synsets.We cover the expansion of the already-established supernsense inventory for nouns and verbs, the addition of coarse supersenses for adjectives in absence of a canonical supersense inventory, and supersenses for verbal satellites.We evaluate the viability of the new senses examining the annotation agreement, frequency and co-ocurrence patterns.