YARN : Spinning-in-Progress (2016)

Abstract: YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing. The first stage of the project was to create noun synsets. Currently, the resource comprises 48K+ word entries and 44K+ synsets. More than 200 people have taken part in assembling synsets throughout the project. The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans.

## introduction the global wordnet association website lists 76 wordnets for 70 different languages foot_0 , including multilingual resources. although the table mentions as many as three wordnets for russian, unfortunately no open russian thesaurus of an acceptable quality and size is still available. the yet another russnet (yarn) project foot_1 started in 2013. it aims at creating a comprehensive and open thesaurus for russian. from the linguistics point of view, the proposed thesaurus has rather a traditional structure: it consists of synsets-groups of near-synonyms corresponding to a concept, while synsets are linked to each other, primarily via hierarchical hyponymic/hypernymic relations. yarn intends to cover russian nouns, verbs and adjectives. following the divide and conquer approach, we treat synset assembly and relationship establishing separately. the main difference between yarn and the previous projects is that yarn is based on crowdsourcing. we hope that the crowdsourcing approach will make it possible to create a resource of a satisfactory quality and size in the foreseeable future and with limited financial resources. our optimism is based both on the international practice and the recent examples of successful russian nlp projects fueled by volunteers. another important distinction is that the editors do not build the thesaurus from scratch; instead, they use "raw data" as the input. these "raw data" stem from pre-processed dictionaries, wiktionary, wikipedia, and text corpora. more than 200 people have taken part in the synset assembly in the course of the project. currently, the resource comprises 48k+ word entries and 44k+ synsets that are available under cc by-sa license. the paper describes the main linguistic and organizational principles of yarn, the tools developed, and the results of the current content evaluation. we also point to some pitfalls of the chosen crowdsourcing methodology and discuss how we could address them in the future. ## related work in this section, we briefly survey projects aimed at creation of wordnet-like semantic resources for russian, describe peculiarities of other thesauri for slavic languages, and systematize different crowdsourcing approaches to building lexicographic resources. ## yarn essentials yarn is conceptually similar to princeton word-net @xcite and its followers: it consists of synsets-groups of quasi-synonyms corresponding to a concept. concepts are linked to each other, primarily via hierarchical hyponymic/hypernymic relationships. ## evaluation we compared yarn with other russian thesauri @xcite that have been described in section 2.1 ( the number of concepts indicates that crowdsourcing is a promising approach for thesauri creation for the russian language. interestingly, yarn contains more concepts than russnet, a project started in 1999. however, when comparing yarn and ruthes-lite, one may notice, that they have an approximately equal number of concepts, yet the number of words in the latter is twice higher than in yarn. this implies the hypothesis that expert-built thesauri include richer lexis that could be covered by non-expert users. hence, the yarn synset quality requires more thorough evaluation. ## conclusion the deliverables of yarn are available under the cc by-sa 3.0 license on the project website 23 in xml, csv, and rdf formats. so far, we have the following plans for the future work.