Recent advances in Word Sense Disambiguation suggest neural language models can be successfully improved by incorporating knowledge base structure.Such class of models are called hybrid solutions.We propose a method of improving hybrid WSD models by harnessing data augmentation techniques and bilingual training.The data augmentation consist of structure augmentation using interlingual connections between wordnets and text data augmentation based on multilingual glosses and usage examples.We utilise language-agnostic neural model trained both with SemCor and Princeton WordNet gloss and example corpora, as well as with Polish WordNet glosses and usage examples.This augmentation technique proves to make well-known hybrid WSD architecture to be competitive, when compared to current State-of-the-Art models, even more complex.

Many knowledge-based solutions were proposed to solve Word Sense Disambiguation (WSD) problem with limited annotated resources.Such WSD algorithms are able to cover very large sense repositories, but still being outperformed by supervised ones on benchmark data.In this paper, we start with analysis identifying key properties and issues in application of spreading activation algorithms in knowledge-based WSD, e.g.influence of the network local structures, interaction with context information and sense frequency.Taking our observations as a point of departure, we introduce a novel solution with new contextto-sense matching using BERT embeddings, iterative parallel spreading activation function and selective sense alignment using contextual BERT embeddings.The proposed solution obtains performance beyond the state-of-the-art for the contemporary knowledge-based WSD approaches for both English and Polish data.

Data augmentation is a difficult task in Natural Language Processing.Simple methods that can be relatively easily applied in other domains like insertion, deletion or substitution, mostly result in changing the sentence meaning significantly and obtaining an incorrect example.Wordnets are potentially a perfect source of rich and high quality data that when integrated with the powerful capacity of generative models can help to solve this complex task.In this work, we use plWordNet, which is a wordnet of the Polish language, to explore the capability of encoder-decoder architectures in data augmentation of sense glosses.We discuss the limitations of generative methods and perform qualitative review of generated data samples.

We propose a novel method of homonymypolysemy discrimination for three Indo-European Languages (English, Spanish and Polish).Support vector machines and LASSO logistic regression were successfully used in this task, outperforming baselines.The feature set utilised lemma properties, gloss similarities, graph distances and polysemy patterns.The proposed ML models performed equally well for English and the other two languages (constituting testing data sets).The algorithms not only ruled out most cases of homonymy but also were efficacious in distinguishing between closer and indirect semantic relatedness.

Neural language models, including transformer-based models, that are pretrained on very large corpora became a common way to represent text in various tasks, including recognition of textual semantic relations, e.g.Cross-document Structure Theory.Pre-trained models are usually fine tuned to downstream tasks and the obtained vectors are used as an input for deep neural classifiers.No linguistic knowledge obtained from resources and tools is utilised.In this paper we compare such universal approaches with a combination of rich graph-based linguistically motivated sentence representation and a typical neural network classifier applied to a task of recognition of CST relation in Polish.The representation describes selected levels of the sentence structure including description of lexical meanings on the basis of the wordnet (plWordNet) synsets and connected SUMO concepts.The obtained results show that in the case of difficult relations and medium size training corpus semantically enriched text representation leads to significantly better results.

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.

In this paper, we compare a variety of sense-tagged sentiment resources, including SentiWordNet, ML-Senticon, plWord-Net emo and the NTU Multilingual Corpus.The goal is to investigate the quality of the resources and see how well the sentiment polarity annotation maps across languages.

In this paper we present a novel method for emotive propagation in a wordnet based on a large emotive seed.We introduce a sense-level emotive lexicon annotated with polarity, arousal and emotions.The data were annotated as a part of a large study involving over 20,000 participants.A total of 30,000 lexical units in Polish WordNet were described with metadata, each unit received about 50 annotations concerning polarity, arousal and 8 basic emotions, marked on a multilevel scale.We present a preliminary approach to propagating emotive metadata to unlabeled lexical units based on the distribution of manual annotations using logistic regression and description of mixed synset embeddings based on our Heterogeneous Structured Synset Embeddings.

Word embeddings were used for the extraction of hyponymy relation in several approaches, but also it was recently shown that they should not work, in fact.In our work we verified both claims using a very large wordnet of Polish as a gold standard for lexico-semantic relations and word embeddings extracted from a very large corpus of Polish.We showed that a hyponymy extraction method based on linear regression classifiers trained on clusters of vectors can be successfully applied on large scale.We presented also a possible explanation for contradictory findings in the literature.Moreover, in order to show the feasibility of the method we extended it to the recognition of meronymy.

In this paper we present a comprehensive overview of recent methods of the sentiment propagation in a wordnet.Next, we propose a fully automated method called Classifier-based Polarity Propagation, which utilises a very rich set of features, where most of them are based on wordnet relation types, multi-level bag-ofsynsets and bag-of-polarities.We have evaluated our solution using manually annotated part of plWordNet 3.1 emo, which contains more than 83k manual sentiment annotations, covering more than 41k synsets.We have demonstrated that in comparison to existing rule-based methods using a specific narrow set of semantic relations our method has achieved statistically significant and better results starting with the same seed synsets.

The paper presents construction of large scale test datasets for word embeddings on the basis of a very large wordnet.They were next applied for evaluation of word embedding models and used to assess and compare the usefulness of different word embeddings extracted from a very large corpus of Polish.We analysed also and compared several publicly available models described in literature.In addition, several large word embeddings models built on the basis of a very large Polish corpus are presented.