Lemmatization is an important preprocessing step in Natural Language Processing (NLP); however, annotated resources for medieval languages such as Old Church Slavonic (OCS) are limited in scope, size, and diversity. This paper presents the annotated resources for OCS lemmatization, including annotation process, design choices and non-standard Unicode related issues. The annotated corpus is used to evaluate existing lemmatization tools (Stanza and UDPipe-2 models trained on the UD 2.12 treebank, and a dictionary-based approach) both in cross-dataset and on a corpus obtained by merging the new annotations with existing UD V2.12 OCS data. Pretrained models perform poorly (≈ 15–16%), below a dictionary baseline (≈ 38%), while retraining on the new data improves performance (up to ≈ 51%) and shows different cross-dataset generalization. Experiments in cross-dataset and on the combined corpus demonstrate that lemmatization performance depends strongly on dataset similarity, annotation conventions, and orthographic mismatch. Overall, the findings show the value of the newly annotated resources and the importance of extending OCS lemmatization benchmarks for historical Slavic NLP.

In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network (CNN) and an Hybrid LSTM. Using the multi-channel CNN we explore the impact of parallel word emebeddings and convolutional layers involving different kernel sizes. With Hybrid LSTM we focus on extracting features in advance, thanks to a convolutional layer followed by two bidirectional LSTM layers. For the second subtask a Transformer BERT-based model (i.e. DistilBERT) and an XLNet-based model are proposed. The multi-channel CNN model is able to reach an F1 score of 0.2928, the Hybrid LSTM modelis able to reach an F1 score of 0.2815, the DistilBERT-based one an average F1 of 0.2165 and the XLNet an average F1 of 0.2296. In this paper, in addition to system descriptions, we also provide further analysis of the results, highlighting strengths and limitations. We make all the code publicly available and reusable on GitHub.