58 research outputs found
Detecting Hate Speech in Social Media
In this paper we examine methods to detect hate speech in social media, while
distinguishing this from general profanity. We aim to establish lexical
baselines for this task by applying supervised classification methods using a
recently released dataset annotated for this purpose. As features, our system
uses character n-grams, word n-grams and word skip-grams. We obtain results of
78% accuracy in identifying posts across three classes. Results demonstrate
that the main challenge lies in discriminating profanity and hate speech from
each other. A number of directions for future work are discussed.Comment: Proceedings of Recent Advances in Natural Language Processing
(RANLP). pp. 467-472. Varna, Bulgari
Complex Word Identification: Challenges in Data Annotation and System Performance
This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes lexical complexity challenging. Our findings show that
most systems performed poorly on the SemEval CWI dataset, and one of the
reasons for that is the way in which human annotation was performed.Comment: Proceedings of the 4th Workshop on NLP Techniques for Educational
Applications (NLPTEA 2017
Challenges in discriminating profanity from hate speech
In this study, we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes -grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalisation, achieving the best result of accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface -grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed
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