In this project note we describe our work to make better documentation for the Open Multilingual Wordnet (OMW), a platform integrating many open wordnets.This includes the documentation of the OMW website itself as well as of semantic relations used by the component wordnets.Some of this documentation work was done with the support of the Google Season of Docs.The OMW project page, which links both to the actual OMW server and the documentation has been moved to a new location: https://omwn.org.

Focusing on recognition of multi-word expressions (MWEs), we address the problem of recording MWEs in WordNet.In fact, not all MWEs recorded in that lexical database could with no doubt be considered as lexicalised (e.g.elements of wordnet taxonomy, quantifier phrases, certain collocations).In this paper, we use a cross-encoder approach to improve our earlier method of distinguishing between lexicalised and non-lexicalised MWEs found in WordNet using custom-designed rulebased and statistical approaches.We achieve F1-measure for the class of lexicalised word combinations close to 80%, easily beating two baselines (random and a majority class one).Language model also proves to be better than a feature-based logistic regression model.

In this paper we compare Oxford Lexico and Merriam Webster dictionaries with Princeton WordNet with respect to the description of semantic (dis)similarity between polysemous and homonymous senses that could be inferred from them.WordNet lacks any explicit description of polysemy or homonymy, but as a network of linked senses it may be used to compute semantic distances between word senses.To compare WordNet with the dictionaries, we transformed sample entry microstructures of the latter into graphs and crosslinked them with the equivalent senses of the former.We found that dictionaries are in high agreement with each other, if one considers polysemy and homonymy altogether, and in moderate concordance, if one focuses merely on polysemy descriptions.Measuring the shortest path lengths on WordNet gave results comparable to those on the dictionaries in predicting semantic dissimilarity between polysemous senses, but was less felicitous while recognising homonymy.

The Global Wordnet Formats have been introduced to enable wordnets to have a common representation that can be integrated through the Global WordNet Grid.As a result of their adoption, a number of shortcomings of the format were identified, and in this paper we describe the extensions to the formats that address these issues.These include: ordering of senses, dependencies between wordnets, pronunciation, syntactic modelling, relations, sense keys, metadata and RDF support.Furthermore, we provide some perspectives on how these changes help in the integration of wordnets.

The paper reports on the methodology and final results of a large-scale synset mapping between plWordNet and Princeton WordNet.Dedicated manual and semi-automatic mapping procedures as well as interlingual relation types for nouns, verbs, adjectives and adverbs are described.The statistics of all types of interlingual relations are also provided.

According to George K. Zipf, more frequent words have more senses.We have tested this law using corpora and wordnets of English, Spanish, Portuguese, French, Polish, Japanese, Indonesian and Chinese.We have proved that the law works pretty well for all of these languages if we takeas Zipf did -mean values of meaning count and averaged ranks.On the other hand, the law disastrously fails in predicting the number of senses for a single lemma.We have also provided the evidence that slope coefficients of Zipfian log-log linear model may vary from language to language.

The paper presents the latest release of the Polish WordNet, namely plWord-Net 4.1.The most significant developments since 3.0 version include new relations for nouns and verbs, mapping semantic role-relations from the valency lexicon Walenty onto the plWord-Net structure and sense-level interlingual mapping.Several statistics are presented in order to illustrate the development and contemporary state of the wordnet.

We describe the release of a new wordnet for English based on the Princeton WordNet, but now developed under an open-source model.In particular, this version of WordNet, which we call English WordNet 2019, which has been developed by multiple people around the world through GitHub, fixes many errors in previous wordnets for English.We give some details of the changes that have been made in this version and give some perspectives about likely future changes that will be made as this project continues to evolve.

The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet.The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synsetlevel mapping of nouns between Princeton WordNet and plWordNet.It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability.Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features.The presented solutions are languageneutral and they can be easily applied to language pairs other than Polish and English.Sense-level mapping is a more finegrained mapping than the existing synset mappings and is thus of great potential to human and machine translation.

It took us nearly ten years to get from no wordnet for Polish to the largest wordnet ever built.We started small but quickly learned to dream big.Now we are about to release plWordNet 3.0-emo -complete with sentiment and emotions annotatedand a domestic version of Princeton Word-Net, larger than WordNet 3.1 by nearly ten thousand newly added words.The paper retraces the road we travelled and talks a little about the future.

This paper presents the results of large-scale noun synset mapping between plWordNet, the wordnet of Polish, and Princeton Word-Net, the wordnet of English, which have shown high predominance of inter-lingual hyponymy relation over inter-synonymy relation.Two main sources of such effect are identified in the paper: differences in the methodologies of construction of plWN and PWN and cross-linguistic differences in lexicalization of concepts and grammatical categories between English and Polish.Next, we propose a typology of specific gaps and mismatches across wordnets and a rule-based system of filters developed specifically to scan all I(inter-lingual)-hyponymy links between plWN and PWN.The proposed system, it should be stressed, also enables one to pinpoint the frequencies of the identified gaps and mismatches.

A wordnet is many things to many people: a graph of inter-related lexicalised concepts, a taxonomy, a thesaurus, and so on.A wordnet makes good sense as the mainstay of any deep automated semantic analysis of text.We have begun the construction of a multi-component, multi-use toolkit of natural language processing tools with plWordNet, a very large Polish wordnet, at its centre.The components will include plWordNet and its mapping onto an ontology (the upper level and elements of the middle level), a lexicon of proper names and a semantic valency lexicon.Some of those elements will be aligned with plWordNet, and there will be a mapping onto Princeton WordNet.Several challenging applications will show the utility of the toolkit in practice.

Lexicalised concepts are represented in wordnets by word-sense pairs.The strength of markedness is one of the factors which influence word use.Stylistically unmarked words are largely contextneutral.Technical terms, obsolete words, "officialese", slangs, obscenities and so on are all marked, often strongly, and that limits their use considerably.We discuss the position of register and markedness in wordnets with respect to semantic relations, and we list typical values of register.We illustrate the discussion with the system of registers in plWordNet, the largest Polish wordnet.We present a decision tree for the assignment of marking labels, and examine the consistency of the editing decisions based on that tree.