pl W ord N et 4.1 - a Linguistically Motivated, Corpus-based Bilingual Resource (2019)

Abstract: The paper presents the latest release of the Polish WordNet, namely plWordNet 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 plWordNet structure and sense-level inter-lingual mapping. Several statistics are presented in order to illustrate the development and contemporary state of the wordnet.

## linguistic motivation since its origin, plwordnet has been built around the idea of making lexical units foot_0 (henceforth, lus) its basic building blocks, using linguistic lexico-semantic relations, and making the wordnet a faithful description of the polish lexical system, see @xcite . this led to a corpus-based wordnet development process @xcite , synset composition based on sharing constitutive relations and features, and wordnet model based on the minimum commitment principle, see @xcite . in plwordnet, the description of lexical meanings is primarily based on lexicosemantic relations directly originating from lexico-semantic relations known from lexicography. as synset relations are abbreviations for the fact of sharing relation between lus -synset components -synset relation do not differ in their character from relations linking lus. glosses and usage examples are treated as secondary means of description. definitions of particular relations directly refer to language data via substitution tests that are then used in the wordnet development. in plwordnet 3.0 @xcite lus located in the lower parts of the hypernymy hierarchy were often described by only a few relation links, if not just one. thus, their meaning descriptions were limited, especially those described by single hyponymy links connecting to the same hypernym. there was no meaning distinction between such lus. diversity and density of relation links is crucial for many applications of a wordnet, e.g. comparison of meanings, analysis of selectional preferences @xcite , word sense disambiguation @xcite , texts semantic indexing @xcite , query expansion in information retrieval @xcite , or construction of topic descriptors for media monitoring @xcite . taking the above into account, we have proposed an expansion of the plwordnet model by several new relations, described in the next section. in sum, we will have 33 types of synset relations (52 when counting subtypes) and 20 types of lu relations, i.e. not shared among lus (56 including subtypes). ## structure since 3.0 version, we have expanded plword-net both in terms of language material covered and the number of relation links, char-acterised briefly in this section and shown in tab. 3. as main goals for the expansion to plwordnet 4.1 we identified: the newest polish vocabulary (and meaning changes) in relation to the whole lexical system of polish and specialist terminology (including multi-word expressions) from users' corpora. concerning the first goal, this is a necessary process for preserving the quality of plwordnet as a comprehensive and up-to-date description of the polish lexical system. continuous development of the coverage of a wordnet is an obligatory aspect for the preservation of its quality. the presence of specialist vocabulary, mostly terminology, in a general dictionary (a large wordnet is often perceived as a large dictionary) is disputable. however, plwordnet is mostly used as a basic language resource in processing, e.g. as the part of the clarin language technology infrastructure, and its content should reflect to some extent the vocabulary of texts being processed. as such, the addition of specialist terminology and vocabulary has been a corpus-driven effort. the development of plwordnet follows the corpus-based wordnet development process proposed in @xcite . plword-net corpus v10 has been enlarged up to 4.2 billion segments in order to make it a better basis for the acquisition of new lemmas. new colloquial vocabulary was added from sources such as social media, blogs, also the most recent literature. several much smaller specialist corpora from the clarin-pl users were also explored as the sources of language material. ## alignment to english a self-contained construction of plwordnet brought about the need of its later alignment to princeton wordnet. the process started in 2012 and has been continued since then. moreover, we have also developed the methodology for a more fine-grained senselevel mapping and applied it to a substantial sample of noun lexical units. the methodology is based on a manual verification of the values of equivalence features. these include formal features such as number, countability and gender; semantic-pragmatic features such as sense, lexicalisation of concepts, register, collocations and co-text; and translational features such dictionary listing, dictionary equivalent position, and translation probability. the features are used to define three types of equivalence links: strong, regular and weak. ## further works is it ever possible to complete a wordnet? plwordnet 4.1 size and coverage, as well as its growth since 3.0 version may suggest that it is. however, this is misleading. in the case of a very large wordnet, the focus shifts from mere growth to the improvement of the amount and quality of information expressed for different lexical meanings. we plan to continue the work on increasing the density of relations (especially for lus described so far by a few, if not single links), continuous maintenance of the wordnet quality by encompassing new lemmas and lus in a corpus-based way. instead of incorporating more and more specialist vocabulary, we plan to develop a system of crossresource mappings envisaged in @xcite in order to build a system of terminological, ontological and knowledge resources around plwordnet and make it an interface between them and the natural language lexicon. in addition, we also plan to further expand the relation structure towards better support for word sense disambiguation. moreover, we are going to continue the works on sense-level mappings. while proceeding with manual mapping, we are also going to develop a semi-automatic prompt system.