- Ondrej Čičkán: Comment classification in community question answering
- Jakub Gedera: Automatic Context-Based Text Reconstruction for Slovak
- Michal Hucko: Clustering and Classification of Student’s Answers to Questions
- Samuel Pecár: Automatic Taxonomy Extraction
- Branislav Pecher, Michal Kováčik, Jozef Mláka, Pavol Ondrejka: Development of Inovative Application in International Competition
- Matúš Pikuliak: Transfer Learning between Languages for Sentiment Analysis
- Andrej Vítek: Online support solution for educational exercises
- Rastislav Krchňavý: Aspect Based Sentiment Analysis
Comment classification in community question answering
master study, supervised by Marián Šimko
Abstract. Community question answering (cQA) portals, such as StackOverflow, have been gaining popularity in recent years. These portals are often unmoderated and it is difficult ot find out relevant answers for your problem in long question thread.
In our work we propose and implement system for automatic clasification of relevant answers to certain question. First, we look at recent works about methods for comparing text similarity. Then we analyse methods used directly on problem of cQA. It was show that solving problem of question answering in cQA enviroment gives us more information than only text similarity. We can use some heuristic rules or analyse relationships with one answer to another, which emergind during discusson.
Dataset for training models and testing is provided by Internation Workshop on Semantic Evalution (SemEval). This shared dataset gives us opportunity to evaluate our solution and compare it to others solution published in SemEval.
Automatic Context-Based Text Reconstruction for Slovak
master study, supervised by Marián Šimko
Abstract. Many journals use sentiment analysis to detect misconduct in the discussions. The problem is that on the Internet dominates non-formal language, which complicate task of sentiment analysis. Typical feature of post is that users use emoticons that have strong impact in sentiment analysis. A lot of negative posts include funny emoticons, which affects the accuracy of the result, and vice versa. People fairly confidently assume that they can correctly identify emotions in text messages. Experiments from Chatham University found that this is certainly misleading.
The aim of our work is to reconstruct and normalize the input text. We mean to determine emotions from text and correctly replace emoticons that have different meaning than text emotion. Detecting emotion from text is a relatively new classification task. To solve this problem, we use emotion detection model. We consider Ekman’s six emotions class (joy, sadness, anger, disgust, fear, surprise).
Finally, we plan to compare success of sentiment analyzer on posts before reconstruction and after using our method that replace emoticons in post based on emotion of post.
Clustering and Classification of Student’s Answers to Questions
bachelor study, supervised by Mária Bieliková
Abstract. We analyse answers from students on questions, in which is impossible to identify finite number of solutions. We use text clustering and classification. We concentrate on ones written in Slovak language, which are just few words long. Our research question is: How can answer clustering help? In our work we apply different methods and algorithms used in text analysis. Using this method with real time presentation services can lead to an improvement on lectures.
Evaluating student’s answers is essential part of teacher’s work. The checking can be time consuming when facing more than hundreds of records. We can see a big lack of automatic methods. It is even impossible to notice some similar parts in answers while going through them one after another. In this case document clustering would be helpful, to monitor similarity in the test answering. Text classification can support teacher in the situations when there is enough labeled data from previous tests. We are working with documents (answers), which are just few words long.
Our main goal is to provide additional information about common mistakes of the class, to person, who is checking results. Gaining this information automatically, could be very helpful with summarizations of the test. Teachers would get structural view of answers clustered in several groups.