Naslov
Combining Shallow and Deep Learning for Aggressive Text Detection

Autori
Golem, Viktor ; Karan, Mladen ; Šnajder, Jan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018) / - Santa Fe (NM), 2018, 188-198

Skup
27th International Conference on Computational Linguistics (COLING 2018) ; First Workshop on Linguistic Resources for Natural Language Processing (LR4NLP-2018)

Mjesto i datum
Santa Fe (NM), Sjedinjene Američke Države, 20.08.2018. - 26.08.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
abusive language, deep learning, ensembles

Sažetak
We describe the participation of team TakeLab in the aggression detection shared task at the TRAC1 workshop for English. Aggression manifests in a variety of ways. Unlike some forms of aggression that are impossible to prevent in day-to-day life, aggressive speech abounding on social networks could in principle be prevented or at least reduced by simply disabling users that post aggressively worded messages. The first step in achieving this is to detect such messages. The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user- generated text on social networks. Our system learns to distinguish between open aggression, covert aggression, and non-aggression in social media texts. We tried different machine learning approaches, including traditional (shallow) machine learning models, deep learning models, and a combination of both. We achieved respectable results, ranking 4th and 8th out of 31 submissions on the Facebook and Twitter test sets, respectively.

Izvorni jezik
Engleski

POVEZANOST RADA