We present our approach to tackle the Sentiment Classification Task. The task was divided into 3 categories: 1) Track A: Multi-label Emotion Detection 2) Track B: Emotion Intensity, and 3) Cross-lingual Emotion Detection. We participate in subtasks 1 and 2 for the Russian language. Our main approach is summarized as using pre-trained language models and afterwords working with fine-tuning aside the corpora provided. During the development phase, we had promising outcomes. Later during the test phase, we got similar scores to the Semeval baseline. Our approach is easy to replicate and we proportionate every detail of the process performed.

Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.

This paper presents our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish, and Slovenian: (1) predicting the change of similarity and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language.