Sense-Annotated Corpus for R ussian (2022)

Abstract: We present a sense-annotated corpus for Russian. The resource was obtained my manually annotating texts from the OpenCorpora corpus, an open corpus for the Russian language, by senses of Russian wordnet RuWordNet. The annotation was used as a test collection for comparing unsupervised (Personalized Pagerank) and pseudo-labeling methods for Russian word sense disambiguation.

## introduction the task of automatic word sense disambiguation is the central task of automatic semantic analysis of texts and consists in choosing the correct word sense in the context of its use. the best results in this task have been achieved through the use of machine learning methods, which are based on preliminary manual annotation of a text corpus by lexical senses. most existing text collections for word sense disambiguation are annotated using sense inventory of wordnet-like resources @xcite @xcite . in this paper we consider a new corpus annotated word senses for russian, which uses the word sense inventory of russian wordnet -ruwordnet @xcite . we also test some baseline methods using the created corpus such as the most frequent sense (mfs), unsupervised personalized pagerank method @xcite , and pseudolabeling based on so-called monosemous relative approach @xcite . ## sense-annotated collection for creating a sense-annotated collection, we use texts collected in the opencorpora project 1 . the opencorpora corpus gathered russian texts and develop several layers of annotation for the open use of these data by researchers (cc by-sa license) @xcite . currently, the opencorpora corpus has a subcorpus with morphological annotation annotated by crowdsourcing. the morphological corpus was used for developing one of the most known russian morphological analyzers pymorphy2 @xcite . but the opencorpora does not contain texts with word sense annotation. ## evaluation of wsd methods on the collection we experimented with two approaches for russian word sense disambiguation: unsupervised pagerank method and automatic pseudo-labeling based on 'monosemous relatives'. ## conclusion we presented a sense-annotated corpus for russian. the total size of the corpus is 109,893 lemmas, out of which 46,320 ones are manually annotated by 8,619 ruwordnet synsets. the obtained corpus was used as a test collection for evaluating two word-sense disambiguation methods: personalized pagerank and pseudolabelling. the precision of ppr is 75.4% and the precision of pseudo-labelling is 74.1%. our future work will be undertaken in two directions: (1) firstly, we are going to use the corpus not only as test data, but also as a training collection for supervised methods. (2) secondly, we are going to further develop the corpus itself, including annotating multi-word expressions and publishing the corpus in the linguistic linked open data cloud. the corpus has been published on github: https://github.com/llod-ru/ opencorpora-ruwordnet.