Mária Bieliková

Also published as: Maria Bielikova


Improving Sentiment Classification in Slovak Language
Samuel Pecar | Marian Simko | Maria Bielikova
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

Using different neural network architectures is widely spread for many different NLP tasks. Unfortunately, most of the research is performed and evaluated only in English language and minor languages are often omitted. We believe using similar architectures for other languages can show interesting results. In this paper, we present our study on methods for improving sentiment classification in Slovak language. We performed several experiments for two different datasets, one containing customer reviews, the other one general Twitter posts. We show comparison of performance of different neural network architectures and also different word representations. We show that another improvement can be achieved by using a model ensemble. We performed experiments utilizing different methods of model ensemble. Our proposed models achieved better results than previous models for both datasets. Our experiments showed also other potential research areas.

NL-FIIT at SemEval-2019 Task 9: Neural Model Ensemble for Suggestion Mining
Samuel Pecar | Marian Simko | Maria Bielikova
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we present neural model architecture submitted to the SemEval-2019 Task 9 competition: “Suggestion Mining from Online Reviews and Forums”. We participated in both subtasks for domain specific and also cross-domain suggestion mining. We proposed a recurrent neural network architecture that employs Bi-LSTM layers and also self-attention mechanism. Our architecture tries to encode words via word representation using ELMo and ensembles multiple models to achieve better results. We highlight importance of pre-processing of user-generated samples and its contribution to overall results. We performed experiments with different setups of our proposed model involving weighting of prediction classes for loss function. Our best model achieved in official test evaluation score of 0.6816 for subtask A and 0.6850 for subtask B. In official results, we achieved 12th and 10th place in subtasks A and B, respectively.


Improving Moderation of Online Discussions via Interpretable Neural Models
Andrej Švec | Matúš Pikuliak | Marián Šimko | Mária Bieliková
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.

NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level Preprocessing
Samuel Pecar | Michal Farkas | Marian Simko | Peter Lacko | Maria Bielikova
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

In this paper, we present neural models submitted to Shared Task on Implicit Emotion Recognition, organized as part of WASSA 2018. We propose a Bi-LSTM architecture with regularization through dropout and Gaussian noise. Our models use three different embedding layers: GloVe word embeddings trained on Twitter dataset, ELMo embeddings and also sentence embeddings. We see preprocessing as one of the most important parts of the task. We focused on handling emojis, emoticons, hashtags, and also various shortened word forms. In some cases, we proposed to remove some parts of the text, as they do not affect emotion of the original sentence. We also experimented with other modifications like category weights for learning and stacking multiple layers. Our model achieved a macro average F1 score of 65.55%, significantly outperforming the baseline model produced by a simple logistic regression.