Senja Pollak

Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style.

We present results of the first gender classification experiments on Slovene text to our knowledge. Inspired by the TwiSty corpus and experiments (Verhoeven et al., 2016), we employed the Janes corpus (Erjavec et al., 2016) and its gender annotations to perform gender classification experiments on Twitter text comparing a token-based and a lemma-based approach. We find that the token-based approach (92.6% accuracy), containing gender markings related to the author, outperforms the lemma-based approach by about 5%. Especially in the lemmatized version, we also observe stylistic and contentbased differences in writing between men (e.g., more profane language, numerals and beer mentions) and women (e.g., more pronouns, emoticons and character flooding). Many of our findings corroborate previous research on other languages.

Simplified Hybrid Approach for Detection of Semantic Orientations in Economic Texts

The aim of this work is to reproduce the approach to detecting semantic orientations in economic texts that was presented in the paper Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts by Malo et al. The approach employs the Linearized Phrase Structure model for sentence level classification of short economic texts into a positive, negative or neutral category from investor’s perspective and yields state-of-the-art results. The proposed method employs both rule based linguistic models and machine learning. Where possible we follow the same approach as described in the original paper, with some documented modifications. Our solution is simplified in at least two aspects, but its performance is comparable to the original and overall remains better than the reported results of other benchmark algorithms mentioned in the original paper. The differences between the two models and results are described in detail and lead to conclusion that the original approach is to a large extent repeatable and that our simplified version does not overly sacrifice performance for generalizability.

RoboCHAIR: Creative Assistant for Question Generation and Ranking

Computational Creativity is a sub-field of Artificial Intelligence research, studying how to engineer software that exhibits behaviours which would reasonably be deemed creative. This paper addresses a creative task of question generation from scientific papers, using a pattern-based approach to finding relevant sentences from which questions should be generated, a natural language processing question construction mechanism, a crowd sourcing mechanism for question rating, and a robot interface for posing questions during a conference session, integrated in a creative Robo CHAIR solution. The system was trained on a set of 200 articles from past computer science conferences and evaluated on a set of articles of members of the local lab.