There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.

Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision. The models trained on such data are effective at identifying the general views of the population. However, their usefulness for predicting the emotions evoked by the textual content in a particular individual is limited. In this paper, we present a study performed on a dataset containing 7,000 opinions, each annotated by about 50 people with two dimensions: valence, arousal, and with intensity of eight emotions from Plutchik’s model. Our study showed that individual responses often significantly differed from the mean. Therefore, we proposed a novel measure to estimate this effect – Personal Emotional Bias (PEB). We also developed a new BERT-based transformer architecture to predict emotions from an individual human perspective. We found PEB a major factor for improving the quality of personalized reasoning. Both the method and measure may boost the quality of content recommendation systems and personalized solutions that protect users from hate speech or unwanted content, which are highly subjective in nature.

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.

In this article we present an extended version of PolEmo – a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).

In this article, we present a novel multi-domain dataset of Polish text reviews, annotated with sentiment on different levels: sentences and the whole documents. The annotation was made by linguists in a 2+1 scheme (with inter-annotator agreement analysis). We present a preliminary approach to the classification of labelled data using logistic regression, bidirectional long short-term memory recurrent neural networks (BiLSTM) and bidirectional encoder representations from transformers (BERT).

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-of-synsets 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.

In this article we present the result of the recent research in the recognition and normalisation of Polish temporal expressions. The temporal information extracted from the text plays major role in many information extraction systems, like question answering, event recognition or discourse analysis. We proposed a new method for the temporal expressions normalisation, called Cascade of Partial Rules. Here we describe results achieved by updated version of Liner2 machine learning system.

We report a first major upgrade of Inforex — a web-based system for qualitative and collaborative text corpora annotation and analysis. Inforex is a part of Polish CLARIN infrastructure. It is integrated with a digital repository for storing and publishing language resources and allows to visualize, browse and annotate text corpora stored in the repository. As a result of a series of workshops for researches from humanities and social sciences fields we improved the graphical interface to make the system more friendly and readable for non-experienced users. We also implemented a new functionality for gold standard annotation which includes private annotations and annotation agreement by a super-annotator.

In this article we present the result of the recent research in the recognition of genuine Polish suicide notes (SNs). We provide useful method to distinguish between SNs and other types of discourse, including counterfeited SNs. The method uses a wide range of word-based and semantic features and it was evaluated using Polish Corpus of Suicide Notes, which contains 1244 genuine SNs, expanded with manually prepared set of 334 counterfeited SNs and 2200 letter-like texts from the Internet. We utilized the algorithm to create the class-related sense dictionaries to improve the result of SNs classification. The obtained results show that there are fundamental differences between genuine SNs and counterfeited SNs. The applied method of the sense dictionary construction appeared to be the best way of improving the model.

In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and lemmatize named entities for Polish.

The aim of this paper is to present a system for semantic text annotation called Inforex. Inforex is a web-based system designed for managing and annotating text corpora on the semantic level including annotation of Named Entities (NE), anaphora, Word Sense Disambiguation (WSD) and relations between named entities. The system also supports manual text clean-up and automatic text pre-processing including text segmentation, morphosyntactic analysis and word selection for word sense annotation. Inforex can be accessed from any standard-compliant web browser supporting JavaScript. The user interface has a form of dynamic HTML pages using the AJAX technology. The server part of the system is written in PHP and the data is stored in MySQL database. The system make use of some external tools that are installed on the server or can be accessed via web services. The documents are stored in the database in the original format ― either plain text, XML or HTML. Tokenization and sentence segmentation is optional and is stored in a separate table. Tokens are stored as pairs of values representing indexes of first and last character of the tokens and sets of features representing the morpho-syntactic information.